Podcasts about Quantile

In this conversation, Jeffrey Bradbury interviews Amy Lau, the Director of Learning at MetaMetrics, about the Lexile and Quantile frameworks and the newly launched Lexile Hub. They discuss the importance of accessibility in educational resources, the functionalities of the hub for teachers and parents, and the significance of Lexile and Quantile measures in assessing reading and math levels. The conversation also touches on the future developments of the hub and the community engagement initiatives that aim to enhance the user experience. If you are a new listener to TeacherCast, we would love to hear from you.  Please visit our Contact Page and let us know how we can help you today! Conversation Takeaways The Lexile Hub is designed to be accessible for all users. Teachers can find resources tailored to their students' Lexile and Quantile measures. Parents can use the hub to understand their child's reading and math levels. The Find a Book tool helps users locate books that match their Lexile measures. Community engagement is a key focus for the future of the hub. The Resource Center offers over 3000 linked math resources for educators. Curated lists can be created for students to facilitate targeted learning. The hub aims to save educators time in utilizing assessment data. Lexile and Quantile measures provide a universal scale for reading and math proficiency. The hub is a one-stop shop for educators, parents, and students. Chapters 00:00 Introduction to MetaMetrics and the Lexile Hub 02:53 Accessibility and User Experience Enhancements 05:52 Understanding Lexile and Quantile Measures 08:50 Tools for Parents and Educators 12:06 The Resource Center and Community Engagement 14:53 Future Developments and Closing Thoughts About Company MetaMetrics Brings Meaning to Measurement MetaMetrics is guided by a powerful north star—to support student growth through actionable learning measurement. Over the last 35+ years, MetaMetrics' staff of educators, psychometricians and policy leaders have developed learning frameworks that now support over 35 million students in the US. More than half of the K-12 students in the US receive Lexile and Quantile measures and over a hundred million pieces of content have corresponding measures. MetaMetrics was founded in 1984 by Dr. A. Jackson Stenner (retired) and Dr. Malbert Smith with the singular goal of making measurement meaningful by matching students to learning resources using a scientific, universal scale. Today, Lexile and Quantile measures are available in all 50 states, either through formal partnership agreements with 21 state departments of education or at the local level through partnerships with edtech companies who deliver services to schools and districts. Levering the most advanced AI technology and learning theory, MetaMetrics continues to innovate solutions for a wide range of applications including early reading, career readiness and tutoring. For more information, visit MetaMetricsInc.com. Links of Interest Website: https://metametricsinc.com/ Lexile Hub: https://hub.lexile.com/ Twitter: @MetaMetrics_Inc YouTube: www.youtube.com/@metametrics LinkedIn: https://www.linkedin.com/company/metametrics-inc-/ Follow Our Podcast And Subscribe

Sina, COO and co-founder of 21st Capital, discusses the application of power law in understanding Bitcoin's growth. He explains how his empirical research led to the development of a power law model that accurately describes Bitcoin's historical price behavior. The discussion delves into the mechanisms behind this model, the reliability of its predictions, and the impact of market maturity on Bitcoin's growth trajectory.  Sina also introduces quantile models to provide a probabilistic view of future price predictions, emphasizing the importance of understanding market dynamics and investor behavior. They also discuss the evolving dynamics of Bitcoin mining, the impact of fiat inflation on Bitcoin valuation, and the significance of the power law in Bitcoin's growth. They deep dive into MicroStrategy's unique position in the Bitcoin market, analyzing its premium and market dynamics, and explore the future interplay between MicroStrategy and Bitcoin. Takeaways

A unique Bitcoin price model called "Quantile Model" charts a potential $285,000 top for BTC in 2025. Learn more about your ad choices. Visit megaphone.fm/adchoices

ストリームにパーセンタイルを計算したい森田が教科書を読みました。

In this episode of the Digital Learning Today Podcast, Jeff welcomes Sue Ann Towle, Vice President, Product Management, MetaMetrics on the podcast LIVE from ISTE 2024 to discuss the new Lexile & Quantile Hub. If you are a new listener to TeacherCast, we would love to hear from you.  Please visit our Contact Page and let us know how we can help you today! In This Episode … MetaMetrics, an innovative leader in educational measurement, assessment, and AI announced at ISTE, the launch of the redesigned Lexile® & Quantile® Hub, set to debut in September. This major update underscores MetaMetrics' commitment to accessibility, enhanced user experience, and increased support. First launched in 2018, the Hub is an online collection of innovative tools and resources that leverage the Lexile® Framework for Reading and the Quantile® Framework for Mathematics. Users visited the Hub nearly 3 million times in 2023 to access learning materials and guidance that bridge the gap between assessment scores and instructional content used in the classroom, saving teachers time and supporting academic growth. The new Lexile & Quantile Hub was rebuilt from the ground up to prioritize accessibility, complying with WCAG 2.1 AA accessibility standards and the Americans with Disabilities Act (ADA). Accessibility requirements like WCAG 2.1 AA are evolving to provide better support and research shows only 4% of companies have a website that meets these requirements. Social media integration further connects the user community, allowing educators and parents to share insights and resources effortlessly. Tools, like the popular Lexile® Find a Book, are now easier to access and faster to navigate for educators and parents who want to locate appropriately challenging reading materials on topics of interest for their students. A new Resource Center centralizes materials supporting instruction to ensure that educators and parents can efficiently find and utilize the information they need. For example, resources like maps that align student measures and content measures, grade level charts, and decodable practice passages are reorganized into a central location making them easy to locate. This saves time for teachers who are supporting differentiated instruction in their classrooms. The redesign also enhances the visibility of features at all subscription levels from the free visitor account through premium partner access. Users can easily create accounts and access resources tailored to their membership tier. Educators in states that have a contractual partnership with MetaMetrics enjoy free access to the highest tier of Hub membership which provides the most access to usage of tools and features. About MetaMetrics MetaMetrics Brings Meaning to Measurement MetaMetrics is guided by a powerful north star—to support student growth through actionable learning measurement. Over the last 35+ years, MetaMetrics' staff of educators, psychometricians and policy leaders have developed learning frameworks that now support over 35 million students in the US. More than half of the K-12 students in the US receive Lexile and Quantile measures and over a hundred million pieces of content have corresponding measures. MetaMetrics was founded in 1984 by Dr. A. Jackson Stenner (retired) and Dr. Malbert Smith with the singular goal of making measurement meaningful by matching students to learning resources using a scientific, universal scale. Today, Lexile and Quantile measures are available in all 50...

PolicyWTF: See No Evil, Read No Evil, Hear No EvilThis section looks at egregious public policies. Policies that make you go: WTF, Did that really happen?— Pranay KotasthaneEarlier this week, I stumbled on this headline in the Business Standard: "Remove price cap and channel bundling restrictions: Broadcasters tell TRAI”. For someone writing a weekly newsletter on Indian public policy, price controls are a gift that keeps on giving. Naturally, I went down this rabbit hole.For context, read this consultation paper. Under the New Regulatory Framework 2017, there are price caps on channel bundles, individual channels that are part of bundles, and the overall package of standard-definition channels. Once this 2017 order came into force, broadcasters smartly kept the popular sports channels out of the channel bundles. The aim was to price them high, thereby cross-subsidising other channels. Further, some providers included these sports channels in bundles at a discounted rate so that they could be packaged with other trashy channels. Not surprising. And now, TRAI wants to reduce the price cap on individual channels that can be part of a bundle to ₹12 from ₹19 per month. Mind-boggling, no?The consultation paper is quite well-written, to be honest. It makes me wonder the extent to which state capacity is applied to come up with price controls. This instance got me thinking about how government restrictions have shaped today’s media environment in India. Let’s have a look at the three major types: video, radio, and written media. How OTT (Over-the-top) became TOT (The-Only-Thing)The same TRAI consultation paper highlights that OTT platforms (SonyLiv, HotStar, etc.) are displacing traditional TV. Anecdotally too, this shift is quite obvious. So why is it that there’s good Indian content on OTT platforms, while the old news channels seem to be stuck in a rut? Government regulations are one big reason. There are no price caps on OTT platforms, allowing them to make investments, create niche content, and recover the investments at an appropriate price. In contrast, TV channel prices are controlled by the government since 2004. News channels, in particular, have degraded the most. Writing in Hindustan Times in 2017, Ashok Malik traced the cause to (surprise! surprise!) price caps again:“As per the TRAI tariff order of 2016, the price ceiling for a news channel is Rs 5 per month. In contrast the price ceiling for a general entertainment channel is Rs 12 per month.Consider what this means. In theory, the general entertainment channel could be re-running old soaps (cost of content: zero). The news channel would be required to constantly generate fresh content. Even so, the former is allowed to charge more than double what the latter is able to. Besides a general entertainment channel is always likely to get more subscribers. So it is a double hit for anybody seeking to build a serious news channel.Over time news channel owners have simply given up, and decided to take the route of reality TV. Today, with the sheer volume of free – occasionally dubious and sometimes outright fake – content available online, one wonders if the news business can ever be rescued in India.”Not that general entertainment channels have fared much better. Broadband internet has now made subscription easier, and the people have voted with their feet, remotes, and phones. At present, TRAI no longer caps the prices of individual channels, on the condition that they are not included in any bundle. But that’s hardly a respite when enough damage has already been done.Radio SilenceThe case of another broadcast medium, the FM radio, is also instructive. The kiss of death here is a ban on FM channels broadcasting news or current affairs. Observe how the government justified pre-censorship in the Supreme Court in 2017:“Broadcasting of news by these stations/channel may pose a possible security risk as there is no mechanism to monitor the contents of news bulletin of every such stations. As these stations/channels are run mainly by NGO/other small organisation and private operators, several anti-national/radical elements within the country can misuse it for propagating their own agenda.”Need I say more? This is the reason why all our FM radio channels play mind-numbing songs, spoofs, and call pranks on loop. While some niche content has moved to podcasts, a lot of current affairs content is now sought after on non-English YouTube channels. As for “radical elements within the country can misuse it for propagating their own agenda”, that has been turbocharged by one-to-many communication on Twitter, WhatsApp, Facebook, etc. The Pen is Mightier than its SubscribersNow let’s come to the curious case of print and online media. There are no price caps on newspaper and magazine prices. Not that it wasn’t attempted. But in a 1961 Sakal Papers vs Union of India judgment, the Supreme Court, citing Article 19(1), declared unconstitutional a law that tried to connect prices to the number of pages published.And so, India has an amazingly high number of newspapers and magazines— nearly a lakh registered ones, increasing year on year. But that’s where the party ends. Print media is disproportionately dependent on advertisement revenue and not reader subscriptions. Newspapers are primarily pamphlets, with a bit of news and opinion thrown in.The reasons for this low equilibrium are not very clear. Raju Narisetti contends in a recent book Media Capture: How Money, Digital Platforms, and Governments Control the News (edited by Anya Schiffrin) that the ‘invitation pricing’ model introduced by the Bennett Coleman & Company Ltd. (BCCL) in 1994 created a de-facto price cap for other players. However, that still doesn’t explain the absence of niche, small, and subscription-fuelled newspapers. Magazines do slightly better. I suspect the low purchasing power of Indians when newspapers were all the rage, can explain to an extent the inertia to pay more for reading news. Whatever the reasons, it works well for India’s governments, for they are the biggest advertisers in newspapers. Mere threats of cancelling advertisement contracts become powerful means to exert influence on the content and tone of newspapers. Nevertheless, online media has shown that new revenue models are possible. In the pandemic, most newspapers took their online portals behind paywalls. There’re also many subscriber-only portals catering to special audiences. But how can you keep the government away? RBI’s new rules on auto-debit of recurring payments led to the cancellation of subscriptions and a decline in revenue. (Showing small mercies, the RBI this week decided to raise the e-mandate limit to ₹15,000 earlier this week.)All in all, if you want to ask why our media environment is the way it is, tracking government regulations is a good place to begin the search. TV and Radio, and to a lesser extent print media, are all victims of seemingly well-intentioned yet counter-productive government regulations. India Policy Watch: Inflation, Growth & StabilityInsights on burning policy issues in India- RSJWe are back to discussing macroeconomy here. This week, in its scheduled bi-monthly review, the Monetary Policy Committee (MPC) voted unanimously to increase the repo rate by 50 bps (100 bps = 1 percentage point) to 4.90 per cent. It also stayed firm on withdrawing its accommodative policy stance to tame inflation going forward. From the Governor’s press release:“Let me now explain the MPC’s rationale for its decisions on the policy rate and the stance. The protracted war in Europe and the accompanying sanctions have kept global commodity prices elevated across the board. This is exerting sustained upward pressure on consumer price inflation, well beyond the targets in many economies. The ongoing war is also turning out to be a dampener for global trade and growth. The faster pace of monetary policy normalisation undertaken by systemic advanced economies (AEs) is leading to heightened volatility in global financial markets. This is reflected in sharp corrections in major equity markets, sizeable swings in sovereign bond yields, US dollar appreciation, capital outflows from EMEs and even from some AEs. The EMEs are also witnessing depreciation of their currencies. Globally, stagflation concerns are growing and are amplifying the volatility in global financial markets. This is feeding back into the real economy and further clouding the outlook.”To put this in context, we have had an almost 100 bps increase in repo rate in about a month. Short-term rates in the market have already moved up by about 200 bps in the last six months. The impact of these will begin to pinch. And yet, inflation remains above 7 per cent and is likely to stay there for a while. There’s been a coordinated response between the government and the central bank in the recent past including a reduction in excise duties on fuel. Some external factors like the lifting of the palm oil exports by Indonesia and a likely good monsoon also might help moderate inflation during the year. But the 6 per cent upper limit of the inflation target range will be breached for most of the year. The Ukraine war and its repercussions on supply chains and commodities have kept prices elevated. The speed of monetary policy normalisation by the developed world has meant the dollar has appreciated sharply, equity markets have fallen across and capital has flown out of emerging markets. The statement by the Governor acknowledged these issues and summarised its priorities (italicised by me below):“Experience teaches us that preserving price stability is the best guarantee to ensure lasting growth and prosperity. Our actions today will impart further credibility to our medium-term inflation target, which is the central tenet of a flexible inflation targeting framework. India’s recovery is proceeding apace, offering us space for an orderly policy shift. While we will continuously assess the evolving situation to tailor our responses, our actions must demonstrate the commitment to keep inflation and inflationary expectations under check. Therefore, monitoring and assessing inflation pressures and balancing risks to growth will be crucial for judging the appropriate policy path as we move ahead. ……Given the elevated uncertainties of the current period, we have remained dynamic and pragmatic rather than being bound by stereotypes and conventions. As the Reserve Bank works tirelessly in its pursuit of macro-financial stability, I am reminded of what Mahatma Gandhi said long ago: If we want to overtake the storm that is about to burst, we must make the boldest effort to sail full steam ahead.”Nothing new there on priorities. For any central bank, they remain to manage the interplay between - price volatility, growth and macro-financial stability. This is an equilibrium hard to locate in normal, calmer weather. In uncertain times like today, it is a gigantic headache. We will dig a bit deeper to understand the variables that RBI will have to deal with in handling these three priorities during the year. First, let’s take inflation. As I mentioned above, the global risks to inflation will remain elevated with high crude oil and commodity prices and continuing supply bottlenecks for the next couple of quarters. The more interesting point here is that the input cost spikes haven’t yet been passed on to consumers in India. You can take a look at the declared results of the Jan-Apr quarter for listed companies to draw this conclusion. As this gets passed through eventually, inflation will keep pushing upwards. The opening up of the high contact services sector is almost complete now, notwithstanding the recent spike in Covid cases in parts of India. So, there is still the impact of services inflation to show up. Globally, central banks have made an about-turn on their earlier views of this inflation being transient. India is no different. The inflation expectations now show a secular upward trend and this is reflected in various surveys like PMI and BIES. Like always, the lower-income bands are starting to voice their concern about prices because it materially affects their lives. Price rise in India is a politically sensitive topic and as much as this government is politically dominant with the opposition nowhere in sight, it is difficult to see how it will remain unfazed by it. An important point to also consider here is the unique K-shaped recovery that’s happened in India post-pandemic. We have spoken about it a few times earlier. This has meant there is further concentration of total consumption among the top 10-15 per cent of India. The problem with this is that it leads to stickiness in prices and wages. This creamy layer of consumers has a low marginal propensity to consume and that combined with the large cushion of savings with them means there isn’t a quick demand-side response to the rising prices in India. Also, a useful question to ask is what is the impact on growth because of a change in real interest rate in India? Is there any historical evidence to find a relation between the two? A rough rule of thumb is that a 100 bps change in real interest rate could lead to a 20 bps drop in expected growth rate ( a summary of a 2013 paper by RBI that concludes this is at the end of this article). This suggests RBI won’t be worried about growth slowdown anytime soon as it raises rates. The government won’t be worried too. Why? Because there is a global slowdown and it can always point to China struggling with its own lockdowns. In any case, we have seen a 4 per cent growth rate just before the pandemic and that had no impact on the popularity of the government. The government will be willing to trade growth for lower inflation. So, the front-loading of interest rate hikes, as seen in the last month, will continue. My guess is, cumulatively, we will have another 100 bps rate hike by the end of this year.  Second, let’s look at growth. The FY23 growth forecast has moderated from 9+ per cent about two quarters back to about 7-7.5 per cent range in most estimates. However, so far the high-frequency indicators of growth are holding up well suggesting robust economic activity. On almost every indicator - from fuel consumption, cement and sale production, exports, IIP, e-way bills or GST - we are up by a significant margin from the pre-pandemic levels (20-30 per cent in most cases). Credit offtake has also been strong in the retail loans segment so far. The recent rate hikes and the correction in the equity market will have an impact on this but we will have to wait and see how soon the slowdown in consumption will show up in numbers. My guess is it will take some time because of the nature of the consumption pyramid in India. There is also spillover effect of the US Fed's action on rate hikes on India. Will India be forced to mirror Fed’s moves? The inflation in the US is at a historic 40-year high and the economy is running at almost full employment. So supply disruptions apart, there are strong demand factors impacting inflation there. In India, there is some overheating in the labour market, especially in the technology space but we are far from any kind of tightening. It will be useful to bring in Taylor’s rule here to understand the likely monetary policy response. From Investopedia:“Taylor's rule is essentially a forecasting model used to determine what interest rates should be in order to shift the economy toward stable prices and full employment. The Taylor rule was invented and published from 1992 to 1993 by John Taylor, a Stanford economist, who outlined the rule in his precedent-setting 1993 study "Discretion vs. Policy Rules in Practice."Taylor's equation looks like:r = p + 0.5y + 0.5(p - 2) + 2Where:r = nominal fed funds ratep = the rate of inflationy = the percent deviation between current real GDP and the long-term linear trend in GDP In simpler terms, this equation says that the Fed will adjust its fed funds rate target by an equally weighted average of the gap between actual inflation and the Fed's desired rate of inflation (assumed to be 2%) and the gap between observed real GDP and a hypothetical target GDP at a constant linear growth rate (calculated by Taylor at 2.2% from approximately 1984 to 1992). This means that the Fed will raise its target fed funds rate when inflation rises above 2% or real GDP growth rises above 2.2%, and lower the target rate when either of these falls below their respective targets.”The current weights for India are 1.2 for inflation and 0.5 for growth while the growth weight for the US might be close to zero. Also, remember we didn’t use the fiscal tools as liberally as the US during the pandemic. The US treasury balance sheet expanded by more than a quarter on the back of the stimulus to prop up the economy in the last two years. We have a very different reality. Of course, there will be some defence of the Rupee that will be needed as the actions of the central banks of the developed markets strengthens the US Dollar. But beyond those temporary shocks of investors looking for a safe haven and creating currency volatility, there should be no real reasons why the MPC should follow the lead of the Fed's response to inflation in the US.Lastly, how will this expedited, front-loaded rate hike actions impact the macroeconomic stability especially of the financial sector? As we have already seen, the transmission of interest rate hikes has happened with speed. Most banks have lost no time in resetting their rates. Also, remember the majority of small business loans to the MSME sector and mortgage loans in India are now linked to repo rates (or some external benchmarks like 30-day T-bills). If the global growth slows and exports weaken and if the large corporations pass on their input cost burden to the customers or their vendors, we might see stress building up in the system among smaller borrowers. This is a lead indicator to be watched although the repo rates after the latest round of hikes are still about 150 bps below where they were in 2018-19. This isn’t a scenario like in the US or UK where the interest rates are at multi-decadal highs. Some prudence on part of borrowers and a bit of flexibility in restructuring loans by Banks aided by the RBI should help the system see through this phase. On the balance, I see the CPI settling at about 5 per cent in four quarters from now. The “neutral” real interest rate should be about 1.5 per cent which would mean a repo rate of about 6.5 per cent. My estimate is that’s where we will end up from the current 4.9 per cent level in about 12 months. That’s when any option of moving back to an accommodative stance will start looking viable. The RBI will be walking on eggshells managing the multiple trade-offs between growth, inflation and macroeconomic stability during this time. Through a happy coming together of circumstances, India is placed relatively better than most economies at this moment. We should avoid any misadventures at this time, political or economic. That’s not a lot to ask for, I hope. Postscript: Here’s the paper from the RBI website - “Real Interest Rate Impact on Investment and Growth – What the Empirical Evidence for India Suggests?”. It is a good empirical study about how much growth sacrifice should be needed to tame inflationary pressure. From its abstract: “Monetary policy is often expected to adopt a pro-growth stance in a phase of prolonged slowdown in growth and sluggish investment activities. Sacrificing inflation, i.e. lowering nominal policy rate even when inflation persists at a high level, is a convenient means to lower real interest rates, which in turn could be seen as a pro-growth stance of monetary policy. This paper, using both firm-level and macroeconomic data, and alternative methodologies - such as panel regression, VAR, Quantile regression and simple OLS – finds that for 100 bps increase in real interest rate, investment rate may decline by about 50 bps and GDP growth may moderate by about 20 bps. The empirically estimated sensitivity of investment and growth to changes in real interest rate suggests that if the RBI can lower real lending rates, it can also stimulate growth. Review of literature highlights that a central bank can lower real interest rates either through financial repression or by not responding aggressively to inflation while raising the nominal policy rates in response to inflation. Empirical estimates for India indicate that RBI’s monetary policy response to inflation has not been aggressive, and as a result the Fisher effect –i.e. one for one response of interest rate to inflation that could leave the real rate constant – does not hold. Thus, even when a high nominal interest rate may often signal that monetary policy stance is tight, because of higher inflation and absence of Fisher effect, lower real interest rate may actually be growth supportive. In India, real lending rates in recent years have been generally lower than the levels seen during the high growth phase before the global crisis. But lower real rates in the post-crisis period have coincided with sluggish investment and GDP growth. This is due to the fact that while real rates are lower, marginal productivity of capital, or expected return on new investment has also declined, which has dampened the expected positive impact of lower real rates on investment. In such a scenario, one policy option could be to lower real rates even more, by raising inflation tolerance, i.e. lowering nominal policy interest rate even when high inflation persists or inflation expectations remain high. This paper, however, provides robust empirical justification against any policy of lowering policy interest rates when inflation persists above a threshold level of 6 per cent. The beneficial impact of lower real rates on growth that may be achieved through higher inflation tolerance is more than offset by the harmful effect of high inflation, particularly when it exceeds a threshold level of 6 per cent.”Matsyanyaaya: Dictatorship and Democracy in Israel and PakistanBig fish eating small fish = Foreign Policy in action— Pranay KotasthaneNews reports suggest that Pakistan’s military dictator-turned-president-turned-politician Pervez Musharraf is in a critical medical condition. While I have no good things to say about the man, I was reminded of a post I’d written in 2017 which asked: despite their similarities, why has Pakistan had bouts of military dictatorship rule, while Israel has steadfastly retained electoral democracy?The two religious States — Israel and Pakistan—were both created for the explicit purpose of securing a homeland for religious minorities. Given their preoccupation with security, the military-security establishment occupied a key position in the politics of the two States. Yet, what can explain this fundamental difference: while Pakistan has had long periods of rule by a military dictatorship, Israel has steadfastly retained electoral democracy?The similarities between Israel and Pakistan are well documented. Faisal Devji’s 2013 book Muslim Zion argues thatLike Israel, Pakistan came into being through the migration of a minority population, inhabiting a vast subcontinent, who abandoned old lands in which they feared persecution to settle in a new homeland. Just as Israel is the world’s sole Jewish state, Pakistan is the only country to be established in the name of Islam.In this regard, the military dictator Gen Zia-ul-Haq’s remarks made in an interview to The Economist in 1981 are also instructive:Pakistan is like Israel, an ideological state. Take out the Judaism from Israel and it will fall like a house of cards. Take Islam out of Pakistan and make it a secular state; it would collapse.So, what explains the difference?My hypothesis to explain the difference is this: the mediating variable between democracy and dictatorship is the status of civil-military relations in the formative years.The basis of this hypothesis is an argument developed in Steven Wilkinson’s excellent book Army and Nation. The book tries to explore why the armies in India and Pakistan—although cut from the same cloth—became such markedly different domestic political actors in their respective democracies. My case is that the arguments mentioned in the book apply equally to the Israel—Pakistan comparison. Here’s how.Wilkinson lists three factors for the difference between the armies of independent India and Pakistan:India’s socio-economic, strategic and military inheritance in 1947 was much better than that of Pakistan. Among other things, Partition worsened the ethnic balance in the Pakistan army while improving it somewhat in the Indian army.The Congress party — unlike the Muslim League in Pakistan — was strongly institutionalised and had a political reach and presence that was difficult to replicate, let alone dislodge.During the first decade of independence, the Indian government took specific “coup proofing” measures: new command and control structures, careful attention to promotions, tenures, and balancing ethnic groups at the top of the military, and attention to top generals’ career pathways after retirement.Now, if these exact factors related to civil-military relations in the formative years are applied to the Israel-Pakistan case, one can see that points (2) and (3) were exactly what David Ben-Gurion and his political forces managed to accomplish in Israel. And hence while Israel managed to retain civilian superiority over its military forces, Pakistan kept having episodic military dictatorships.The follow-up question would then be: was Jinnah’s death immediately after Pakistan’s formation a big reason for the path it took, while India and Israel had the benefit of dominant, long-standing civilian leaders in the formative years?I don’t think so. If Jinnah would have lived longer after Partition, it is likely that he would have put specific “coup proofing” measures in place [point (3) in Wilkinson’s schema]. However, the worsening ethnic balance of the army and a weakly institutionalised Muslim League [points (1) and (2)] would’ve still remained intractable. The paths that Israel and Pakistan are now on have a lot to do with what happened in the formative years of the two democracies.HomeWorkReading and listening recommendations on public policy matters[Article] The EU has agreed to make “One Europe, One Charger” a reality in 2024. In October 2021, we had written why this move is a PolicyWTF. The decision is also a useful case study for policymaking. It demonstrates that we should be wary of intuitive solutions to policy problems.[Book] Media Capture: How Money, Digital Platforms, and Governments Control the News (edited by Anya Schiffrin).[Podcast] Ashok Malik speaking about TV price controls on The Seen and the Unseen[Podcast] Shruti Rajagopalan and Lant Pritchett have released another blockbuster Ideas of India episode. A must-listen for all public policy enthusiasts. If you are short on time, jump to Pritchett’s criticism of the poverty line. It’s superb. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit publicpolicy.substack.com

In this paper, we provide an in-depth analysis of how to tackle high cardinality categorical features with the quantile. Our proposal outperforms state-of-the-art encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long tailed or skewed distributions. 2021: Carlos Mougán, D. Masip, Jordi Nin, O. Pujol https://arxiv.org/pdf/2105.13783v2.pdf

Il nono episodio introduce importanti funzioni per descrivere una variabile aleatoria, come quella di ripartizione, quella di densità o quella di quantile.Parliamo anche di Valore a Rischio, e chiariamo la differenza tra scenario e previsione.[Episodio dedicato a Peter Carr. Ciao Peter.]

In this episode of the TeacherCast Podcast, Jeff sits down with Sue Ann Towle from https://metametricsinc.com/ (MetaMetrics) to discuss the https://educatoracademy.lexile.com/ (Lexile & Quantile Educator Academy), a new self-paced professional learning program to help teachers learn how to have a deeper understanding of Lexile and Quantile frameworks. To receive a http://educatoracademy.lexile.com/ ($10 discount, please use the code "TeachCast") If you are a new listener to TeacherCast, we would love to hear from you. Please visit our http://teachercast.net/contact (Contact Page) and let us know how we can help you today! In This Episode ...Both the Lexile and Quantile courses offer an interactive curriculum featuring direct instruction, knowledge checks, summative assessments, and performance tasks. Each course is 10-hours, self-paced and asynchronous. Upon completion of the professional development courses, teachers become Certified Lexile or Quantile Educators. In addition, in more than 20 states, teachers will be awarded CEUs or professional development hours on completion of each course. Summer has long been the time for teachers to take advantage of professional development opportunities, particularly options that are self-directed. A https://www.rand.org/pubs/research_briefs/RBA196-1.html (recent study by Rand's American Teacher Panel) revealed that 99 percent of teachers surveyed said they participated in professional learning activities over summer break. http://educatoracademy.lexile.com/ () “After facing the challenges of the 2020-2021 school year, teachers tell us they are looking for professional development opportunities to help individualize instruction when they return to the classroom in the fall,” said Malbert Smith, CEO and co-founder, MetaMetrics. “With our Lexile and Quantile certification courses, teachers can leverage the measures their students are likely already receiving through a state or classroom assessment and learn about strategies and resources for implementing individualized instruction.” Teachers who complete the courses will gain a deeper understanding of how to make the Lexile and Quantile frameworks actionable for learning and discover award-winning tools and resources on the Lexile or Quantile Hub to support their efforts. They will also learn strategies for communicating effectively with parents about student performance and growth and ways to use the universal measures with their colleagues. Educators who participated in a preview of the Quantile Certification Course had high praise. “This has been a very valuable course. I had zero background in all things related to Quantile measures,” said Laura Graham, elementary teacher at Pender County Schools in North Carolina, “This will help me better plan for my students, sharing with my colleagues, and communicating with parents. The myriad resources allow me to expand my knowledge of teaching mathematics.” Each course is $99. For information about volume discounts, contact support@lexile.com. To enroll or learn more, go to http://educatoracademy.lexile.com (EducatorAcademy.Lexile.com). Follow our Podcast and Subscribehttps://www.teachercast.net/episodes/teachercast-podcast/ (View All Episodes) https://podcasts.apple.com/us/podcast/the-teachercast-podcast/id546631310?mt=2 (Apple Podcasts) https://www.google.com/podcasts?feed=aHR0cDovL2ZlZWRzLmZlZWRidXJuZXIuY29tL1RlYWNoZXJjYXN0Q2FzdFBvZGNhc3RGZWVk (Google Podcasts) https://www.stitcher.com/podcast/teachercast-podcast/the-teachercast-podcast-network-your-educational-professional?refid=stpr (Stitcher Radio) About CompanyMetaMetrics is an award-winning education technology organization that offers the only scientifically valid, universal scales for measuring silent and oral reading and listening (Lexile) and math (Quantile) with plans to develop measures for writing. The Lexile and Quantile Frameworks measure student ability and the complexity of the content they...

James: Hi, audience. This is James Kandasamy. You're listening to Achieve Wealth Podcast through Value at Real Estate Investing. Today, we have an awesome guest. His name is Nikolaï Ray. He's who's the founder and CEO of MREX, which is an acronym for Multifamily Real Estate Exchange; is considered by many of his peers in North America as the leading expert in apartment investing with over $1 billion analysis, underwriting and transactions. He's also a pioneer in mid-cap, multifamily financial engineering, which is, you know, he's regarded as the teacher, advisor and also the keynote speaker. He's also a real estate tech innovator to his current work on the multifamily real estate big data, artificial intelligence and property tokenization using blockchain technology. Hey, Nikolaï, welcome to the show.   Nikolaï: Hi, James. Thanks for having me.   James: Okay, so do you want to mention anything that I missed out about your credibility?   Nikolaï: No, that sounded like a mouthful.   James: It's going to be ready technology-centric discussion today, right?   Nikolaï: Yeah, the full story is that it should probably a lot longer, but I mean, that could be for, that could be for a whole other episode of the origin story of how, how'd you get to, you know, how you get to where we get in life, and professionally and personally, but yeah, that's, that's the gist of it, you know, everything that's underwriting and, you know, acquisitions, dispositions, refinancing, obviously, portfolio management, whether it be the small market, small cap market, you know, between 500 units, all the way up to the mid-market, you know, market cycles, and obviously, have a very strong penchant for data and for technology.   So, so that's, that's pretty much what I've done over the last, I guess, over the last seven or eight years, is focused on, you know, for the most part, I focused mostly on acquisitions. So I was in charge of an investment banking firm, we worked, you know, on both sides of the transaction advisory side of things, for investors and we also work with a lot of ultra high net worth investors, that's kind of where I built my speciality. Eventually, ultra high net worth investors and private equity firms and family offices, you know, by doing all that I kept on, kept on getting annoyed with the fact that the multifamily market is so fragmented, and the data is so packed, I just kept on thinking to myself, you know, this, this market this, which is an important market, I mean, the apartment building investment market is a almost a $10 trillion market worldwide.   It's a, quite, house is a primary need of human beings, which is to have somewhere to live. And yet, you know, we're kind of in the dark ages as multifamily investors, because number one, we don't have access to any centralized marketplace. If you compare us to a stock investor who can go on the NASDAQ and trade every type of tech stock or stock market investing world, the New York Stock Exchange, and we don't have access to any data, the data is very raw, it's very, it's kind of, you know, what I call legacy data, as you look at like Costar and, and all these various data providers who provide this very raw and inert data, without any actual, you know, context around the data, and without any helps with regards to making decisions business intelligence wise, as a multifamily real estate investor. So that's kind of how that's how my career has gone so far. That's why I went from transactions and more towards data technologies because I felt like there was so much work to be done to help investors just you know, be better investors for once.   James: Okay, so let me understand MREX because I think it's important since you have a lot of passion we need right now. Right? So --   Nikolaï: Yeah.   James: Multifamily Real Estate Exchange, if I understand it correctly, so what you're saying is right now, the data is so fragmented, and a lot of times when, you know, people like me underwrite deals, we have to do so much work, I did too. I mean, I really learn to write [inaudible 04:05] for four hours because I did all the property management financial, that there are so much of mistakes in the property management financials, you have to do T-3, T-12, you had to do expense ratio, you have to do market comps, and all that. So what you're saying is, you are going to summarize all that, and make it so easy to look at so that it can be treated as a commodity, commodity, is that right?   Nikolaï: Not necessarily. So, so the idea is taking you as an example or any of your listeners, right now, who are multifamily real estate investors actually acquiring properties, let's say you have the capital ready, or your investors have the capital ready to allocate to an acquisition, you know, just actually finding that first property to buy or the next property to buy is a very time intensive and energy intensive job, right. You have to go on, you have to go on all the different MLS, you have to go on the loop that's of this world, the [inaudible 00:05:00] and the [inaudible :00:05:01] and, you know, just --   James: [inaudible00:05:02]   Nikolaï: Right, and then you have all the brokers, and then you have all the broker websites, then you have all the pocket listings and you have not even really touched the majority of the market, you're actually still missing probably, you know, anywhere between 25% and 50%, of actual transactional inventory, depending which metro area you're in. So it's a lot of work, even just looking at the stuff that's on websites. That's a lot of work because you have to go on between five and fifteen websites, each website has a different user interface, this different user experience, and actually shows different information. On one site, maybe on [inaudible 00:05:42] you might have a cap rate, maybe on the MLS, you won't have cap rate, you'll just have gross revenue.   So then you have to figure out your own cap rate off of that. It's a lot of work, you know, and for me, I just never thought it made sense, to not be able to say, hey, I want to buy a multifamily property, whether it be a five unit, whether it be a 50 unit or 500 units, I want to go on to one marketplace, we're all properties are centralized in a unified, and normalized manner. Because that's the second point of it, is you have to be able to normalize expenses, if you want to start comparing apples with apples, and oranges with oranges. So that's the second phase. So what we're doing with MREX is we're building a unified, standardized marketplace for multifamily investors, where they will be able to see every single property that exists, that is for sale, despite on the way it's being sold or listed or marketed. We're going to be working with brokers obviously, the goal is not to get rid of brokers or anything like that, that's not, that's not what our goal is. Our goal is to help brokers, help investors just make the whole transaction process much quicker and more time efficient. And that way, you know, we're making the market more, you know, just a more efficient market.   James: Okay, okay. Got it. Got it. So you are basically streaming lining the whole selling and buying process, I guess, just to make --?   Nikolaï: Absolutely. Absolutely.   James: Okay, got it.   Nikolaï: And the analysis process as you said too, right, because it's one, it's one thing finding the properties and having them all in one marketplace. Okay, let's say, let's say you have the NASDAQ, let's say I wanted Lesson TechStars rather than multifamily properties. I go the NASDAQ and I can see every single company, I could have access to inventory, now that's the first step. Now the second step is, once you have access to inventory, and the information provided on all that inventory is normalized and standardize, well, I still have to be able to start comparing and start, you know, building my own models to say, well, if I'm a cash flow investor, which stocks are generating the most cash flow relative to the other, to the rest of the inventory. So that's where you know, context and alternative data comes into play with our platform, is that we want to be able to, to offer data and tools to you as a multifamily investor, to help you streamline your underwriting of the inventory that you've seen. So that's really the two things we're focused on at the moment.   James: Okay, got it. Got it. So interesting. So that'll be, that'll make a lot of, I mean, for investors or for buyers, they would be able to see what kind of deals that they want to buy,--   Nikolaï: Right.   James: Not just what they want to get the yield out of --   Nikolaï: Exactly and instead of going on fifteen websites, well, they've only one website, instead of having to, you know, start normalizing expense ratios and sifting through, through T-12 and T-3, and doing all that, it already kind of be all chewed up and kind of built up already. So you can actually focus, focus on analyzing, focus on comparing and establish, okay, I want to buy this property using this strategy. And why would I do that versus the other property that I see over there? That's ultimately what's the most important thing.   James: Okay, okay. So could it then be a good idea to match this with a crowdfunding platform, because during the crowdfunding, they can choose what deal they want, right?   Nikolaï: Right. So crowdfunding is an interesting thing. The problem is crowdfunding, obviously, crowdfunding, crowdfunding has tried to kind of attack two things. Number one is liquidity, right? Because, as a multifamily investor, the more properties that you acquire, you increase your net value, right, you're a richer person. But the problem with that, is that you have to leave equity in every single deal, right. The banks won't finance you 100%. So you always have to leave equity. So as you get richer and richer, value wise, you are actually cash poor, because you're leaving so much equity in each property that you acquire. And there's always a part of the equity that has to stay in those properties. But the problem, the second problem is that as you get, as you become a bigger investor, and you acquire more properties, and you're more well known in the market, well, you get access to better deals, but now you have less access to more money, even though you're richer. That's kind of the liquidity conundrum of multifamily investors. So that's why crowdfunding is interesting, because it gives kind of, you know, after the JOBS Act, it helps multifamily investors, particularly syndicators, to go and raise capital from, you know, from investors either through the regulation CF, you know, and obviously, regulation D506C was quite an upgrade also to be able to start to, to market capital raises. But what we're doing is we're actually building a second platform that is shadowing the Emirates platform. And what that platform will be doing is, we're actually going to create a sort of stock market and take the crowdfunding thing a bit further, because crowdfunding, as I said, tries to attack the liquidity conundrum. But the problem is, is that when you invest in a crowdfunding deal, you as an LP, are stuck in that deal for the lifetime of the deal. So if it's a five, it's a three to five year exit, well, your money stuck in that, so you, you as a passive investor, or as an LP, do not have liquidity. That's, that's one problem. And obviously, crowdfunding also helps with accessibility, right. So obviously, regulation D506C is only for accredited investors, which doesn't really help accessibility that much. Regulation CF has helped that because now then, that kind of lowers the barrier to entry for everyday retail investors who don't have that much money, but it's still a fairly limited regulation. At the moment, I know, they're trying to pass a couple of bills to increase the opportunity for regulation CF investors. So what we're doing is we're building a second platform, that's going to be basically a stock market, in its own sense, where, you know, through a broker-dealer partner that we hope to get. And then also through eventually a, an ATS license with the SEC, we would like to be able to take it a step further, and allow a multifamily investor to pretty much offer his property through one the various regulations on that marketplace. That way people could invest as passive investors, as LPs, either through Reg D, Reg CF, or eventually maybe even Reg A plus, but then they would also be able to acquire or access a secondary trading market so that they're not stuck in an illiquid period of three to five years. They would actually eventually be able to re trade part of their shares or all of their shares, kind of like you would at the stock market.   James: Wow. So it looks like you are trying to really disrupt the industry.   Nikolaï: Yeah, definitely. [inaudible 00:12:36]. You know, multifamily real estate looks like the stock market before the arrival of NASDAQ. Right? It's like before the internet, even though we have internet and multifamily real estate, it's as if people are still trading kind of like stock market investors were trading on floors, you know, with papers and screaming and doing all that stuff. It, you know, it doesn't make sense.   James: Yeah, yeah. It's so private nowadays, right? I mean, everybody has priority, we do not know how, even multi families performing under a different private LLC.   Nikolaï: Exactly.   James: There's a lot of good news out there. But there's also bad news, but nobody talks about it. right. So I think,--   Nikolaï: Oh, right. And the data, the data out there, like look at any of the data from, you know, even from the really big organization like NCREIF so the National Council of Real Estate Investment Trusts, NCREIT sorry. Even their data, when they know these indexes based on multifamily markets is based on a very low volume of the actual number of transactions. So when say a, a company, various data company says, well, the cap rate right now of say Atlanta is 5%, for example, well, that's actually based on a very small portion of overall transactions. So it's hard for us as multifamily investors, to really be sure are about the numbers that we're inputting into our underwriting models, because we're basing it off so little data.   James: Got it. Got it. Yeah, it's, it is just so limited, right? Because everything is done on a private basis on syndication, which is not much of the data being published out there, right. So --   Nikolaï: It's like investing in the stock market, but not knowing how the stocks have performed historically.   James: Yeah. Correct. Correct. So but why do you think this would work? And because if you look at the demographics of the, I mean, because I'm looking at syndication, when we whenever we buy for multifamily.   Nikolaï: Right.   James: But for me, it's just a small part of the whole market.   Nikolaï: Right.   James: Even though we are I mean, maybe my group or my network thinks that that's the whole thing how people buy multifamily. I don't know, that's true, because I network with a lot of different type of people, right. So looking at the classes of investors who are buying multifamily, I think I know for me, my thing is maybe we are one of the, I am one the lowest level part of it, right, because we are buying Class B and C using high net worth individuals and all that, but there are a lot of higher network, higher calibre people who are playing at a different level, which we don't have, which I don't have visibility, maybe you have it right so. So are you trying to look at different classes of investors and cut through all of them? Are you looking at only some classes of people?   Nikolaï: So we're trying to help what we call the small cap to mid middle market investors.   James: Okay.   Nikolaï: So anyone who owns between five units and about, you know, I'd say around 2500 to 5000 units.   James: Okay.   Nikolaï: That's kind of where we stopped, you know, that's where we're focusing on because that, you know, the majority of transactions are actually done by, by small cap to mid-market investors.   James: Okay.   Nikolaï: You know, the multifamily market is historically a mom and pop market. Now, it's, you know, it has transition a bit, investors are getting bigger and bigger. But the reality is the majority of the market is not an institutional market, you know, at the root level, or the private equity firm level or family office level, depending obviously, which metro area you're in, right. New York City is obviously more of an institutional market. Canada, Toronto is a very institutional market, but the majority of cities and metro areas are still, you know, very small cap market. And the problem is that, you know, take you for an example as a syndicator, or even take someone who's not a syndicator, right, because a lot of investors, multifamily aren't syndicators, they just buy their own properties, you know, they end up with maybe, you know, anywhere between 50 and 500 units as time goes by. Now, the problem with with those types of investors and syndicators as yourself is that you do not have access to a team of underwriters, you don't have access to, you know, expensive data that say a real estate investment trust has more than a very big private equity firm has, you don't have access to all those analysts. So, you know, we want to try and make sure that the market stays very level and stays is a level playing field. Because, you know, ultimately, I think the multifamily real estate market is very important for a couple of reasons. Number one, you know, everyone talks about the disparity of wealth, right of the 1%, and how the disparity is getting bigger and bigger. And we could do a whole podcast on that and why it's happened and where it's kind of going. But ultimately, I think, you know, the multifamily market is probably, the market, it's probably the asset class that offers the best returns based on risk, with the best risk-adjusted returns. If you look at Sharpe ratios, and Sortino ratios and all these things. Now, it's also been proven, there's a lot of studies about this, a lot of university studies done on this, that, you know, social mobility comes from education, and access to property, right. The reason why people have been so poor for so long, and like the Brazilian favelas, or the Indian shanty towns, is because people don't have education, and they do not have access to property, they are not able to become landowners, or owners of their own homes, even less become investment property owners, right. So I think multifamily stays as a very important asset class, because, on top of filling a basic need of human beings, that means providing somewhere to live, it also is a very important mover, for the everyday investor, the mom and pop, just the normal person need you to be able to access a very good, very safe, wealth building asset class that does not have the same volatility, or the same pitfalls as say, the stock market and other types of asset classes. So I think it's very important that we provide, you know, tools and data and allow for the smaller investor, the investor that has less than 1000, or even less than 5000 units to be able to continue on performing, continue on from this, this asset class.   James: Got it. Got it. So let's go to a bit more details on some of the big data and artificial intelligence, right.   Nikolaï: Yeah.   James: So yeah, I studied artificial intelligence almost 24 years ago, every now it has become really popular, a lot of startups with artificial intelligence, right.   Nikolaï: Absolutely.   James: So the question is, how do you, I mean, first of all, let's define what, can you define artificial intelligence in your terms in terms of real estate? Because I studied engineering standpoint.   Nikolaï: Yeah, well, I'm not an engineer, by trade, so at least I'll give more of a generalist definition to the people listening which I think is probably gonna be very good. The important thing is to understand, kind of the difference between machine learning and artificial intelligence. So you know, machine learning is more of a, it's a less automated process, right. So a lot of what people are calling artificial intelligence is ultimately just machine learning. And what it is, is that let's say, let's say, you know, I'm a data scientist or an economist, and I build a predictive model using, say, Monte Carlo simulations. Well, I set a, I build a set of hypotheses, I plugged them into my Monte Carlo simulation, and then that runs. Now, with machine learning and artificial intelligence, what becomes very fun as you know, statistics are a funny thing, right? And economic modeling is a very funny thing because even though, you know, people in the economics world swear by predictive analytics, the reality is in data science, it's garbage in garbage out, right. So the outputs always depend on the inputs. So let's say you're doing an underwriting model, and you're looking at an apartment building, and and you say, well if I buy this apartment build in this way, my internal rate of return is going to be 25%. Okay. Now, internal rate of return, net present value is a, is an output or their outputs based ultimately on the strength of those outputs are only as good as the strength of the inputs.   James: Correct.   Nikolaï: And the very important inputs that affect an IRR and NPV, which ultimately led to two of the most important metrics to help you decide whether it's a buy a property or not are rent growth, expense inflation, refinancing interest rate; if your IRR and NPV is based on on refinance, because obviously IRR and NPV has to be based on an exit model. And the exit model can either be a refi or it can be a sale; disposition. And then if it's a disposition, while your IRR and NPV is based, ultimately off the reverse, the reversion cap rates, so the exit cap rate upon sale. Now what everyone's doing right now, in the multifamily market, especially small investors, and mid-market investors is they're just entering these inputs. You know, they're just playing it by ear, and they're not even playing it by ear. They're coming up with these random inputs that are based off absolutely nothing. I just had a huge discussion on LinkedIn about this, with a couple of investors where one guy was saying, well, you know, if I buy it at 5% cap rate, my underwriting model, what I do is, to establish the reversion cap rate. So the cap rate upon eventual sale, let's say five years, is I add 20 basis points to the purchase cap rate per year. So if I bought it at five today at a 5% cap rate, well, then five years from now, I predict that I'll sell it as 6% cap rate, okay. And, you know, people kind of hide behind this type of rule of thumb model, say, well, I'm being conservative, therefore, my underwriting models very good. The reality of it is your underwriting model is bullshit. Okay. It's not worth the the Excel spreadsheet that it's been written upon. The reality is, where are you pulling this, this expansion of 10% or 20%,10 or 20 basis points per year? What are you basing that off? Right? That's what anyone should be asking, What are you basing this off? While being conservative. How do you know you're being conservative?   James: Yeah.   Nikolaï: How do you know you're not being optimistic? Right? You could be being you could actually be very optimistic with that. And conservative might be and then an increase of 0.25 a year, right? The reality of it is that everyone underwriting deals, right now, they're not basing their inputs off any data, right. And they're definitely not basing it off any predictive analytics, because it's one thing to have the data, the historical data. But you know, just because you have historical data doesn't mean necessarily, that's going to repeat itself in the future. That's why we have predictive analytics. So let's say that based on historical data, your 5% acquisition cap rates will actually be a 5.5 in five years. Now, the problem with that is that the future, that history is never guaranteed of the future, right. So that's why you then have to plug in various scenarios where you're considering this. And that's where predictive analytics come very difficult because you're pretty much just kind of taking a shot in the dark and basing things off the past, but you're putting in like a margin of error. With machine learning and artificial intelligence, you're able to make your predictive models better ex post based on ex ante results. So let's say you create a model to predict the future cap rates, well, you want to predict the future cap rate of in five years, it's your goals to sell within five years. Well, if you predict that today, the probability that your five-year cap rate from now is going to be precise, is a lot lower than let's say, in four years, you predict the cap that same cap rate, right, because you'll be closer to your exit. So there'll be less room for margin of error. So what machine learning and artificial intelligence will allow you to do is to consistently kind of reset your model as time advances. So maybe your initial model based upon acquisition was off. But as you advance in time, the artificial intelligence and machine learning continues on training that same model, the same algorithm that you had, and adapts the various inputs and algorithms to make it more and more precise as you get, as you get closer. And on top of that, as you get closer, the range of distribution of property probabilities get smaller. So it's a double effect, your predictive models get even tighter and tighter as time goes by. And that's where [inaudible00:26:03] machine learning and artificial intelligence can really help out. Is that instead of just plugging in these ridiculous exit cap rates, and ridiculous growth rates and ridiculous inflation of expenses, and absolutely ridiculous refinancing interest rates, when we get closer and closer to being able to actually put in inputs that are based on something very, very solid and then, therefore, our underwriting models will become more and more precise. And what we want in underwriting when you're buying a property, whether you're a syndicator, and you're responsible for money of your LPs, or whether it's your own money, the goal of underwriting is not to be conservative. That's not what the goal of underwriting is. And anyone who says that they underwrite, and they're concerned, their underwriting is conservative, what they're really telling you is they don't know how to underwrite, okay.   James: Yeah.   Nikolaï: You don't want to be conservative, you want to be right on the dot, that's what you want to do with underwriting, you want to be as precise as possible because the reason that you buy the property today is you buy it for future cash flows. And cash flows can come in various ways, they come in an annualized cash flow so, so free cash flow, they come in the appreciation of the asset, so the value of that asset gains because of various market dynamics and because of the way you're, you're managing that property. And they also come through the capitalization of your mortgage. So there's a part of your mortgage that you're paying down, which is principal, right. So those are the three cash flows that you can receive. Now, when you're underwriting a deal, and you're looking at how much you should pay for, say, this hundred unit building you're looking at, well, if your inputs are off, you might buy that property. But it's a bad acquisition because you were too optimistic in your inputs. But it also happens that you were too conservative in your books, therefore, you didn't buy the property. Because if you input that at the exit capital, that property is 7%, but, in reality, five years from now, the exit cap rate is five and three quarters, well guess what? You missed one hell of an opportunity.   James: Correct.   Nikolaï: And in real estate investing, the most important thing is time value of money, we only have a very limited time during our lifetimes in which we can invest and create wealth. And we only have so many hours during the day. Therefore the cost of opportunity, the time value of money are the things that we should consider the most in our underwrite. And that's really where machine learning and artificial intelligence will help investors become much, much better. Obviously, you also need education, right? You have to understand these, I mean, this is advanced stuff. And I'm trying to kind of explain it in a simple way, where people who don't have master's degrees and PhDs in finance and engineering can understand it. But the reality of the matter is that multifamily investing is very, it's a very complex, it's a very sophisticated asset class, and you need a certain level of education.The problem being right now, despite the very high level of education that some investors have, we just don't have solid, predictive analytics tools and data to be able to make sure that we're actually able to transfer education into decent acquisitions.   James: Yeah. Well, that's very interesting, because exit cap rate is always being misused or mis-conservative right? So --   Nikolaï: Well, even entering cap rates, even acquisition cap rates, I see people saying, well, you know, I'm not gonna buy that property because it's a five cap rate and the markets trading at 5.5. Okay, is that a stabilized property? No, it's a value add property. Well, the cap rate doesn't, the cap rate is meaningless then. A cap rate is a metric of a stabilized asset. If the asset is not stabilized, there is no cap rate, because a cap rate is a perpetual annuity. It's a return metric, based on an unlevel perpetual annuity, which means the same cash flow every year forever.   James: Correct.   Nikolaï: Now, if you want to be able to calculate that your property has to be stabilized. So if you're not buying a property, because it's a five cap rate, and the market sharing at 5.5, but it's a value add deal, well, I'm sorry, I'm sorry to tell you, you should change, you should change fields, you should go play, you should go to Las Vegas and put it on red.   James: Not only that, I mean, not only new investors don't understand the entry cap rate doesn't matter [inaudible 00:30:46] and I don't know, I never see a reason not to do a stabilized deal. Not on commercial, right? So for me, I'm always [inaudible00:30:53] guy, that's why I --   Nikolaï: Well, unless you're a private equity firm or your family office or you're a RET or you're an ultra high net worth individual who now has, you know, net value of anywhere between ten and hundred and fifty million dollars, there's no real reason to do stabilize deals, right. The reason you wanted to stabilize deals is, because you have a very high net worth, or because you're trying to de-risk your portfolio. Right?   James: Correct.   Nikolaï: That's why you would just stabilize deals for small cap or mid cap investor.   James: Yeah, yeah. Most of the time. I mean, commercials always value at play. I mean,   Nikolaï: Of course.   James: I mean, there's a lot of people doing stabilized deal nowadays, just by getting a higher mortgage and getting slightly lower price, play on the mortgage side with the interest to get a cash flow, but --   Nikolaï: And that can work if you're a neurosurgeon, right? If you're a surgeon making a million and a half a year, and you're 35 and you say, well, you know, I want to start buying multifamily property because I like, I like real estate and I like the tangible part of the asset class. But I don't need any money right now, because I'm making a million, I'm making a million and a half a year. I don't need any cash flow. And I'm very long term and I just want to build myself a nice retirement, you know, because you know, that's what I want as objective. Well, then yes, buy stabilize property or be an LP and syndication, or purchase that stock in the [inaudible00:32:23], that's fine. But if your goal is to increase your wealth exponentially, in a short period of time, and what I mean by a short period of time is fifteen to, five to fifteen years. Well, then, yeah, you're gonna have to do some kind of value add, you can't just do financial arbitrage all the time.   James: Yeah. Yeah, there's a lot of deals out there in different asset class, which can give you that cash flow, right. I mean, you can buy a stabilized mobile home park, you know, it'll give you higher cash in cash than any multifamily deals.   Nikolaï: Right.   James: So even self-storage, or even multifamily, which has been stabilized, you get, you'll get good cash flow. But how long will that cash be guaranteed? Because you have a very tight DSER at that point of time. And let's say the market turn, you may not be, your DSER might be compromised right now, because you don't have any buffer. Right?   Nikolaï: Especially if you did not properly manage the terms of your mortgages. Right. So that's very dangerous. Like if you feel that you're, if you feel that the markets going to shift, say interest rate wise, the easiest way to kind of pull yourself out of that situation you just talk about is, you know, just take longer-term mortgages, you know, make sure that the mortgage does not end in five years, make sure it's a 10 year term, or even maybe a 30 year term. Right? That's, that's the easiest way to manage that risk.   James: Yeah, just do a hard loan.   Nikolaï: Right.   James: Which gives you like, 45 years. I mean, there's the other trick that a lot of people play is, you know, showing you need cash in cash based during IO period. And nowadays, people are getting five years, seven years, IO period and sometimes people think, oh, I will not hold, you know, that deal for long term. I mean, you are hoping on not holding, holding, right. But you do not know what's going to be happening to the economy, right?   Nikolaï: It's a dangerous game to play. And I'm not saying don't play it, but make sure you have the, make sure you have the education and the know-how to be able to manage that risk. It's all risk management. Ultimately, that's what it is.   James: Yeah, yeah.   Nikolaï: The problem, the problem is a lot of people are doing this, and they don't know what the hell they're doing.   James: Yeah, I mean, I think so there's so much of capital out there right now, looking for money to be placed in some way.   Nikolaï: Oh definitely.   James: And people don't think that are they going to putting 1% in the CD, I might as well put here and get like six, seven per cent, right? Cash Flow, right? And,--   Nikolaï: And that's, that's the retail market. Like that's, that's small investors like me and you the reality of is the real cap, the real capital flow right now is at the institutional level, there is so much higher level money and smart money searching for returns right now. I mean, we can't even fathom small investors, how much money, I mean, family offices, typically, if you take the family office market, typically always allocated maybe like, I don't know, depending on the family office in the region, but usually anywhere between, you know, maybe eight to twelve per cent of their overall asset allocation, capital allocation to what they call alternative assets, right. And real estate as part of alternative assets. Now, over the last 10, I'd say over the last 10 years, the last decade, family offices have become more and more in tune to the real estate markets. High net worth families also, especially towards like multifamily real estate, and more and more real estate is no longer considered just as, as something under the alternative asset umbrella. But now it's kind of becoming its own umbrella. And what that's doing is that instead of family offices, and we're talking about family offices that have trillions of dollars, right. These are not these are not small things, these are big moving bodies with a lot of capital, we're talking about multi-billions of dollars, not trillions, multi-billion dollar family offices, that are now instead of allocating, you know, 8% to real estate, well, now they're allocating 20% to real estate. So and that's, that's a scale like, there's a lot of them out there. And we haven't even talked about the private equity firms. We haven't even talked about the pension funds, the International pension funds, you know, people talking about globalization and international money, thinking that it's just, you know, rich Russians is going to Sunny Isles, Florida, buy $10 million condominiums. That's not what it is. The global movement of money to American and Canadian Real Estate are things like the Amsterdam teachers pension fund, or government workers pension fund, you know, allocating, allocating, you know, 100 billion dollars to the American real estate market. Now that's, that has a big, that puts a big dent on the supply and demand of real estate. And that's what ultimately drives property value is much more than interest rates. Interest rates only, only influence property values, like people were talking about, especially the last couple of years, all we know, if interest rates go up, cap rates will follow up, they'll go up. That's not true. Capital flow drives cap rates and values and properties and multifamily; interest rates only influence cap rates and values.   James: Very interesting perspective, that's you are right. There's so many, too much money, even out of United States is looking for money to place, right. Like the other dad had a call from the UK. It's a family office who want to invest in the UK and they're looking for like operators like me, and I was asking them, what's the return expectation? They say this 22% IRR credits and I said, well, I [inaudible 00:37:58] you guys, I can get better money in the United States right, so --   Nikolaï: Exactly. And all the, all the money from the quantitative easing the follow the 2008 crash, I mean, all that quantitative easing money, a lot of it still, after even 10 years, has not even found a place for it yet. Right? So there, there's a lot of money chasing deals, there's a lot of money chasing deals.   James: Correct. Correct. Right. That's true. That's true. So coming back to the exit cap rate. So I know that's one of the hardest parameters to measure. Right? So.   Nikolaï: Absolutely.   James: But can you clarify again, how did you, how would you use artificial intelligence to find that a more accurate exit cap rate? You know, T minus five, my T minus 5, five years earlier, before you hit that five years mark of selling, assuming five years of selling.   Nikolaï: So it's the computing power, right. So it's a computer, what we do is, we'll build, so we'll do we'll say, I'm sorry for anyone who hasn't studied, you know, high level university finance, but or statistics, you know, we'll build a, say, a regression model. So we'll look at past data. We'll plug all that in, in order to build a predictive model, a future model being able to come out with future cap rates, and, you know, the more data that we're able to plug into our regression model. So historically, what real estate institutions and economists have use is what they call the linear regression model, use the Monte Carlo simulations. Now, the problem with the linear regression model is that you know, past transactions or data are, are, are also affected a lot by various things like, you know, political environment, and capital markets. And there's a whole bunch of factors. So there's a new model that's being used more and more, especially with a lot of postdoctoral students in statistics, it's called a Quantile regression model. So that's where we're able to create that same kind of, I'm saying this in layman's terms as much as possible, we're able to take past historical data, build that kind of linear model, kind of, like build that line chart for people to understand, and we kind of repeat that line chart in the future. But we're also able to start to weigh that those data points with various things like a new government, with quantitative easing, with the war, with various factors that may be affected that models to make it less linear. And then we're able to start to better predict future stats and future cap rates. So that's the first step of it. The second step is, let's say, right now, we built our Quantile regression model. And now we compute it and what it says to us is well, T minus five cap rates, or five-year cap rate is going to be between, let's say, we have a couple of tracks, it's hard to explain to people who have not done statistics. But we have a couple of tracks. And ultimately, what it says is that the highest probabilities are that cap rate is going to be between 5.75 and 6.10% in five years for that specific market. Now, like I said, as we get closer to the five year period from now, the less the margin of error is, because we're closer and multifamily market moves very slowly. So predicting, the easiest way to understand is predicting 25 years out from now, it's very hard? Your 25 year prediction is going to be way more, there's more room for it to be completely off than your two-year prediction. So we build a model for the five-year prediction, and then starting tomorrow, every day, our artificial intelligence recalculates that model. So as it recalculates, the model gets more and more precise, because let's say we took statistics from today to 20 years ago, let's say we took the cap rate of that market, starting from today, and 20 years back. Well, obviously, the next 20 years are not going to be exactly the last 20 years. But that's ultimately what statistics do, we try and kind of say, well, let's take the last 20 years, there's a margin of error, that's what's going to be the next 20 years.   So what's cool with the artificial intelligence is without actually having to do anything, every day, the artificial intelligence kind of brings the model a day closer and adapts the model with more and more weight on what's going on right now, rather than what happened 20 years ago. And the artificial intelligence is also able to measure what today it predicted for yesterday, versus what actually happened. And what's the spreading difference and what caused that spread? And therefore, once it's able to determine what caused that spread, it'll add that into the equation for the future cap rate model so it becomes much more precise.   James: Yes, but don't try to run it in iteration on a daily or monthly basis to watch the whole investment process. But how do you make it on day zero? Well, today we're buying today how does it iterate then when on a day zero?   Nikolai: Well, what it is I don't understand the question.   James: So my question is, you said the data is being fed into the system to get more accurate exit cap rate. But you're making a decision to buy today? Is the iteration happening from today to all the investment cycle? Or do you do it earlier before you decide to buy a deal?   Nikolai: Okay, I understand what you mean. So like, for determining your actual purchase cap rate,   James: Yes, correct whatever price that I'm going to pay today because that's what I'm getting into the deal. That's the point of me making a decision, whether this is a good deal, and I'm going to be raising money and telling everybody it's a good deal.   Nikolai: The purchase cap rate is a whole other set of statistics and data models. That's more I'd say, determining today's cap rate is much more endeavor of collecting more historical data. Because like I said, let's say JLL Jones Lang LaSalle which is one of the biggest brokerages, they come out with reports and say, Okay, well, the cap rate, let's say in Austin is, 5.2%. Let's say the mean cap rate is 5.2%. Well, that's based on maybe what like 30 or 40%, of actual transactions that happen because they don't have data on like the off-market transactions, or the pocket listings or this and that, right. And on top of that, they haven't normalized the cap rates on whether, let's say, a building traded at a 4.6 cap rate. Well, as we said, if that property wasn't stabilized, well, then that cap rate is off. That's not a good cap rate. So that's a second thing. So for establishing what you should pay to the intrinsic, what's intrinsic value today. that's ultimately what I think the question is, and correct me if I'm wrong, but let's say you're looking at a 100 unit property, what is the actual intrinsic value of that property? What's the real capital I should be buying at? Well, that's a question of having the proper volume of data, Okay, number one. So that's what we're working on right now is making sure we keep on building our database. So instead of our market cap rates being based on the off 30 or 40%, of inventory, or transactions. Well, it'll be based off maybe 60, 70, 75%, therefore, that cap rate becomes more precise. Secondly, we actually look at every transaction and say, qualitatively because that's the first thing is a quantitative aspect, in statistics, we have quantitative, qualitative. So the quality of the data, once we have the quantity, we look at the cap rates and say, okay, that property traded for a 4.2 cap rate. Was that a stabilized property? No, it was not. Once we add the cap x, we have the new revenues. And we adjust the sales price for cap x, but we also adjust NOI. Now we can look at the stabilized cap rate. So that's the qualitative aspects of it. And now we're able to say, here are the market cap rates, here's the low end of cap rates, here's the high end of cap rates, here's the mean, or the media. And here's that range of cap rates. Because cap rates are based on the Capri calculation ultimately, even though people think it's NOI divided by sale price, I'm sure that's not what a cap rate is, that's how you find the cap rate of a soul stabilized property. The actual cap rate calculation or formula is a mathematical equation of R minus G, it's algebra, so are being returned minus g, which is growth. And R is defined as RF plus RP. So the risk-free rate plus the risk premium that you as an investor are looking for or that the market is looking for, a perceived risk premium, obviously. So what we want to do then, that would be like a third step, and we're not at that level right now. But I hope within the next couple of years, we will be, and I'm sure you as an engineer, probably understanding how valuable our ability to do that would become for the market. Is that then you're starting to be able to say, well, right now, that property is being listed at a say, let's say the range for cap rates in Austin is really five to six, obviously, six is going to be in the worst neighborhoods. Five is going to be the best neighborhoods because it's a matter of risk. Well, then you're looking at the property, let's say it's at a 5.7 cap rate. But it's kind of on the limit of a bad neighborhood, good neighborhood. And then you're able to intrinsically say, but the intrinsic cap rate of that property, the real intrinsic value of that cap rate is actually 5.3. Now, if you didn't know that, and you just said, well, the average cap rate is 5.7 well, it's not so much of a deal, I'm not gonna buy that property. But now with this new data, what you're able to see is, wait a minute, it looks more expensive than what it should be but in reality it's not, it's actually cheaper because the real intrinsic value is a 5.3 cap rate. And that would really unlock the potential of what we call value investing, what like a Warren Buffett has built his entire career off of the stock market? Well, he was able to build that value investing exists so much, in the stock market, because of the quantity and the quality of the data. The quantity of data is accessible to everyone, the quality of the data is a bit harder to get the qualitative aspects. That's why Warren Buffett was has been such a great investor, because he invested so heavily into being able to pull out the qualitative aspects of the data, well, now we would be able to do the same thing, you would be able to do the same thing as a multifamily investor. You would have access to the quantity of data needed for you, then to increase your knowledge based on the qualitative aspects of it, and then be able to properly price that acquisition. And then once you're able to do that, well, then you can go say to your investors, look, this is why I'm buying this deal. This is why it's a good deal. And if on top of that, you're able to be more precise with your exit cap rate, and the growth rates of your revenues and expenses and your refinancing rates. Well, you're going to be a much more confident investor.   James: You are making it really what you call a --   Nikolai: It's a more efficient market.   James: It's a more efficient way of actually determining your purchase because you can really just say generally, Austin is what five cap, it's not true, [inaudible00:50:46].   Nikolai: It's kind of scary to say, but we're all kind of invested in multifamily kind of half blindfold. The guys like me and you, and there's a whole bunch of other guys out there really intelligent wrestlers. We're all invested, based on intuition experience, a very strong knowledge base. But we're ultimately kind of invested with one eye closed. Now it's even worse for people who don't have our knowledge base and experience because they're all invested in completely blindfolded.   James: Interesting. So, if you can get that kind of data where you can look at the stock market, and what's the potential, especially if it's in the path of growth. And what's the risk that you're buying? There are some deals, even though you buy it at the lowest cap rate for that market, it could be still the best growth because it could be just like another big explosion, in terms of jobs, is going to be happening in that area just because of the path of growth.   Nikolai: That's so important because if you're a pro forma and you're underwriting you predicted a 2% growth rate in revenue. But in those five years, the analyze growth radio was six. Well, you probably didn't buy that property, when you should have. And the other thing is the same if you predicted a 6% growth rate, and it was two, then you bought that property you shouldn't have, But what most people will say is well, the guy who predicted 6%, he should have put in 2%, like he should have been conservative, but that's not necessarily true. That's a half-truth. That's actually a mistake in logical reasoning because the other guy who says, I'm going to plug in a 2% growth rate because that's what historically happens. What happens if you invest in a market where the growth rate is actually 6%? And that the other intelligent investors knew or predicted that it would be 6%, while they're willing to overpay, according to you for a property, and then you're not buying anything, you're not generating any returns, you're not building your wealth, and you're just kind of sitting on the sidelines there, Bah, humbugging saying, well, the markets paying way too much for the properties and these guys are stupid, stupid money, blah, blah, blah, I'm going to wait for the market to crash and blah, blah, blah, I know guys who've been saying this since 2012. And they have not bought anything since 2012. They haven't generated any returns. All under the pretext of being conservative investors. You know what, they're not conservative investors, you know why because they're not investors. They haven't bought anything, because they take themselves out of the market, and they're sitting on the sidelines, and they're just making up for lack of precision in their underwriting through, this kind of pseudo-conservatism.   James: I think it just depends on the sophistication of the investors. If you look at nowadays, multifamily has become so popular, so many people who did not have the financial education background or the way to analyze a deal. There's a lot of parameters that go into any deals. That's what you mentioned, you mentioned so many parameters, nobody will look at that. Everybody said multifamily is good. I bought it and it went 300%. And they say, Oh, I'm a really good operator. Well, actually, you should have made 500% because the market gave you at least 400%. 100%, you just did 300%, why did you do 300%?   Nikolai: That comes down to what we call the search for alpha. We want to outperform the market. And all these people and there's a whole bunch of them now there's gurus and mentors and coaches, and they're giving all these online classes or seminars or whatnot, or they're boasting about being such great real estate investors. And the reality of it is they don't even know what they did. They're like, well, I generated X percent returns, and I've created X amount of millions of dollars in profit over the last five and 10 years. But that's actually quite average. That's what the market does, as long as you are in the market. Of course, that's what you generated. Now, did you generate more than what the market did? That's the real question. And unfortunately, there are not enough people in the market asking that question. And if you're a passive investor, that's the question you should be asking your syndicator or your GP is not this is what you generated, great. That sounds awesome. You generated 22% IRR annually over the last five years. What did the market generate? The market generated 23.   James: I remember the other day I saw someone, he said, I made 60%. In one year, I bought it in the first year and I sold it in twelve months, I made 60%, I said well, you should have made that 100% because the market went up by that much.   Nikolai: And that's why I'm so bullish on education, and why I think it's so important that multifamily investors get educated and push their knowledge base, because, this is not Nintendo, this is not Xbox, we're not just playing, baseball on our PlayStation three, or Playstation four, this is serious business, and even more, so if you're syndicator. Just in the knowledge base, you know needs to continuously be expanded. And that's why data also needs to be there because knowledge without data is also quite useless.   James: Correct. So coming back to being the alpha in the market. I know you can look at different market appreciation versus how much you are making money. So coming to, let's say, for a decision where you have a deal in your hand, and you're deciding whether you want to sell or you want to refile, or you 10:31 exchange. So can you give us a good methodology to do to make that decision?   Nikolai: To make the decision on whether you beat the market or...   James: Whether you want to sell a deal, or whether you want to refinance, whether you want to hold it for long term or you want to do a 10:31 exchange? How would you approach it?   Nikolai: Well, I'd approach it on a very individual basis. Number one, I think everyone has a very different investor profile. What I mean by investor profile is, what type of returns do you want? And when? What are the strengths and weaknesses that you possess as either an owner-operator or syndicator or whatnot? What access to capital do you have? How patient is that capital? What's the cost of the capital? Now, if it's your own money, obviously, it's probably the most patient money with the cheapest cost of capital. If you're raising money from other people, well, then obviously, there's a less patient aspect to it, and the cost of capital is going to be higher. If you're taking money from bridge loans, well, that's even worse. So if you're taking money from hard money lenders, well, then obviously, your cost of capital is going to be very, very high. So these are all things that you have to consider, you also have to consider where you are in your career with regards to what it is that you want to achieve, either as annual cash flow or just overall that value and what type of risk you're willing to accept.   So ultimately, you have to be able to answer those questions initially, to be able to decide on the strategies. Because ultimately, people in multifamily investing, what they do not understand is the difference between philosophy and strategies. Now, everyone should have their own investment philosophy, based on their investor profile. Now, once you have that philosophy, what you want to do is adapt your strategies according to where you are in the market, and where you are in your career. That's something that is very misunderstood. People say, I'm a buy and hold investor. We hear that a lot in multifamily. So ultimately, what you're saying that you do not have an investment philosophy, that you think you do. You think your philosophy is to buy and hold. But buy and hold is not a philosophy, it's a strategy. So what you're saying is, ultimately, you're investing all the time throughout the whole of your career, using just one strategy. That's very dangerous because let's say the exit point of that strategy eventually, say the day that you do have to sell upon retirement because even though you're buying a whole, you might not be a legacy buy and hold investor. What I mean by that is a legacy buy and hold investor is someone who's just going to pass down the properties to their children, upon death, or upon retirement, whereas most buy and hold investors, what they really need is, I'm going to buy and hold until my retirement, then I'll start selling off. Well, what happens if, during your retirement, you're in a trough of the market cycle. What if you're in that part of the market cycle, or you're at the bottom of it, that's a really bad time to sell? Well, that's the mistake of always investing using only one strategy. So what I would say is that you have to establish your philosophy, understand that your investor profile is going to change over time. And the market cycle moves through phases, there are different phases of the market cycle and your strategies, you have to be able to use different strategies at different phases of the cycle, and at different phases of your career as your profile changes, or adapts or morphs. And that's how you then establish well, with this property, should I buy it and hold it or should I sell it? Or should I just refinance it? What should I do? And I'll give you a very concrete answer. Once I've explained all this.   I have a student here because I do teach real estate investing courses. We actually built a college we call it The College of the Emmerich's. Now you don't have to, it's not college level education. But what we're saying is that from everyday multifamily investors, if you really want to learn college level stuff without having to go to college, well, we have a couple of courses that we teach you very high-level stuff, very concrete work. You still need coaching from coaches and mentors and all that stuff. We actually teach courses. So one of my students in these courses, he's a very successful real estate investor in Montreal, Canada, Montreal is the most important multifamily market in Canada. It's a very strong multifamily market, very competitive. Now he's up to about I guess, 150 units, all on his own, no outside money, no passive money. And he started having trouble refinancing out of his properties because what he was doing, it seems a very big value add investor. So he was using two strategies value added buy and hold. But he was erroneously thinking that value-added and buy and hold was his investment philosophy, which is not, those are two strategies that are part of the philosophy. So he came to me and he said, well, look, banks have now started to tighten their DSCR ratings, and their LTV, therefore, I'm buying a property at a billion dollars, and putting in $300,000 into it. And now the market value of that property is $2 million. But I'm not able to refine it $2 million, because of the banking standards, they're only allowing me to refine out of 1.6. So now, if they're letting you refine out at 1.6, on a 75%, LTV, what they're saying is when you have to leave in 25% of 1.6 plus $400,000, that's a lot of equity, that it is unable to pull out because he was doing too much of a good job at value add. And the capital markets, the banks are not able to follow market value, banks, especially in Canada, are much more conservative than in the US, but even in the US, there is a lot of people buying properties. And they're not able to refine the whole value, because their total loan dollars are blocked by either LTV or DSCR. What I call economic value, the economic value is not as high as market transaction value. Therefore, instead of leaving 25% of equity, you're leaving 25 plus, in this case, $400,000.00. Now that's where I said to him perfect, I looked at his portfolio, I said, well, you have to adapt your strategies, you have to change the strategies, you can no longer at this moment, use the buy and hold strategy, you have to use the fix and flip strategy.   Because you're too good at fixing value add. And you're not able to pull out as much equity as you used to be through refinancing. Therefore, now you have to seriously consider selling that property. Because you can go and get $2 million for other markets right now. So that's an extra $400,000. Because he was able only to refinance 1.6 out of it. So now he's able to get the full market value, pull that cash out, and he has access to a lot of opportunities. He has a really strong bird document work. So his cost of opportunity is very high. If he's leaving all that equity, in these properties that are all stabilized, he's making way more money by doing more value-add stuff. So he made the decision and now he holds zero properties. He sold all of his 140 units because that has allowed him to get more and more cash rich, with less and less money and equity and properties and gain access to more and more opportunities. And ultimately, his annual portfolio, the total return on investment is in the 40 to 70% IRR. Whereas while he was doing buy and hold his overall portfolio was only returned to him maybe 20% if you consider the weighted average return on investment. So that's how I would attack that. I know, that's a very long-winded answer.   James: I think that's the right answer. So I mean, the return on equity, which is date right now, I mean, on this deal. There's so much of dead equity not producing cash. And if your cost of capital, which is also equal to an opportunity outside is much higher, you might as well just cash that out by selling it off.   Nikolai: Because the refinancing is living you to a liquid.   James: Recently, I mean the banks have been more stringent on refine. So the last refine they did ask me to leave 5% my cash basis, which they never did in the past, things have changed. I think that's okay. That's how the banks work now.   Nikolai: It's okay. But the problem is that on a $15 million property, you know, that's two and a half million dollars less cash you have for the next acquisition.   James: Correct. I mean, it depends on what is the cost of capital outside plus how much you can pull out and how much your equity stuck on it. So, coming back to market cycles, because I think this is one thing that I want to ask you because I think you have studied with Dr. Glenn Mueller. So right now, if I look at the latest Q1 forecast for apartments in the hyper supply market. I don't know if that's something that you are aware or not, but...   Nikolai: Nationally?   James: Nationally yes it's not a local, but lots of markets are in it for supply. It's very, very few markets are in the expansion cycle. And even though they are in the expansion cycle, they are at the last stage of the expansion cycle. And all the markets that are on expansion cycle, or the market that recovered late like Las Vegas, Phoenix and a lot of Econo markets. So can you give an overview of what do you think the market is? And what would the strategy be for investors now?   Nikolai: Well, I think number one, I would say that I try not to look at national or macro market cycles. I think that's the first thing to consider. Because multifamily real estate is so hyperlocal. So I look much more at those markets, cycles of hyper supply and expansion and contraction, I look at more of like a metro area. So like you're in Austin, Texas, I look at Austin, I wouldn't really consider the multifamily market at large, because it's kind of like looking at cap rates on an unstabilize property, it's kind of a waste of time. Now, I'd say that I haven't looked at recent data of where all the cycle, where all the markets are, the phases of the cycle. But I mean, I think it is safe to say that, most of the markets right now are in the later phases of the game, or later innings, as Howard Marks likes to say, in the stock market and capital markets. But also, as he says, we don't really know, see the thing with market cycles, and whether it be with Dr. Mueller, whether it be with Karen Trice, out of Australia, and also all the other various professors and researchers of market cycles, is

James: Hi, audience. This is James Kandasamy. You're listening to Achieve Wealth Podcast through Value at Real Estate Investing. Today, we have an awesome guest. His name is Nikolaï Ray. He's who's the founder and CEO of MREX, which is an acronym for Multifamily Real Estate Exchange; is considered by many of his peers in North America as the leading expert in apartment investing with over $1 billion analysis, underwriting and transactions. He's also a pioneer in mid-cap, multifamily financial engineering, which is, you know, he's regarded as the teacher, advisor and also the keynote speaker. He's also a real estate tech innovator to his current work on the multifamily real estate big data, artificial intelligence and property tokenization using blockchain technology. Hey, Nikolaï, welcome to the show.   Nikolaï: Hi, James. Thanks for having me.   James: Okay, so do you want to mention anything that I missed out about your credibility?   Nikolaï: No, that sounded like a mouthful.   James: It's going to be ready technology-centric discussion today, right?   Nikolaï: Yeah, the full story is that it should probably a lot longer, but I mean, that could be for, that could be for a whole other episode of the origin story of how, how'd you get to, you know, how you get to where we get in life, and professionally and personally, but yeah, that's, that's the gist of it, you know, everything that's underwriting and, you know, acquisitions, dispositions, refinancing, obviously, portfolio management, whether it be the small market, small cap market, you know, between 500 units, all the way up to the mid-market, you know, market cycles, and obviously, have a very strong penchant for data and for technology.   So, so that's, that's pretty much what I've done over the last, I guess, over the last seven or eight years, is focused on, you know, for the most part, I focused mostly on acquisitions. So I was in charge of an investment banking firm, we worked, you know, on both sides of the transaction advisory side of things, for investors and we also work with a lot of ultra high net worth investors, that's kind of where I built my speciality. Eventually, ultra high net worth investors and private equity firms and family offices, you know, by doing all that I kept on, kept on getting annoyed with the fact that the multifamily market is so fragmented, and the data is so packed, I just kept on thinking to myself, you know, this, this market this, which is an important market, I mean, the apartment building investment market is a almost a $10 trillion market worldwide.   It's a, quite, house is a primary need of human beings, which is to have somewhere to live. And yet, you know, we're kind of in the dark ages as multifamily investors, because number one, we don't have access to any centralized marketplace. If you compare us to a stock investor who can go on the NASDAQ and trade every type of tech stock or stock market investing world, the New York Stock Exchange, and we don't have access to any data, the data is very raw, it's very, it's kind of, you know, what I call legacy data, as you look at like Costar and, and all these various data providers who provide this very raw and inert data, without any actual, you know, context around the data, and without any helps with regards to making decisions business intelligence wise, as a multifamily real estate investor. So that's kind of how that's how my career has gone so far. That's why I went from transactions and more towards data technologies because I felt like there was so much work to be done to help investors just you know, be better investors for once.   James: Okay, so let me understand MREX because I think it's important since you have a lot of passion we need right now. Right? So --   Nikolaï: Yeah.   James: Multifamily Real Estate Exchange, if I understand it correctly, so what you're saying is right now, the data is so fragmented, and a lot of times when, you know, people like me underwrite deals, we have to do so much work, I did too. I mean, I really learn to write [inaudible 04:05] for four hours because I did all the property management financial, that there are so much of mistakes in the property management financials, you have to do T-3, T-12, you had to do expense ratio, you have to do market comps, and all that. So what you're saying is, you are going to summarize all that, and make it so easy to look at so that it can be treated as a commodity, commodity, is that right?   Nikolaï: Not necessarily. So, so the idea is taking you as an example or any of your listeners, right now, who are multifamily real estate investors actually acquiring properties, let's say you have the capital ready, or your investors have the capital ready to allocate to an acquisition, you know, just actually finding that first property to buy or the next property to buy is a very time intensive and energy intensive job, right. You have to go on, you have to go on all the different MLS, you have to go on the loop that's of this world, the [inaudible 00:05:00] and the [inaudible :00:05:01] and, you know, just --   James: [inaudible00:05:02]   Nikolaï: Right, and then you have all the brokers, and then you have all the broker websites, then you have all the pocket listings and you have not even really touched the majority of the market, you're actually still missing probably, you know, anywhere between 25% and 50%, of actual transactional inventory, depending which metro area you're in. So it's a lot of work, even just looking at the stuff that's on websites. That's a lot of work because you have to go on between five and fifteen websites, each website has a different user interface, this different user experience, and actually shows different information. On one site, maybe on [inaudible 00:05:42] you might have a cap rate, maybe on the MLS, you won't have cap rate, you'll just have gross revenue.   So then you have to figure out your own cap rate off of that. It's a lot of work, you know, and for me, I just never thought it made sense, to not be able to say, hey, I want to buy a multifamily property, whether it be a five unit, whether it be a 50 unit or 500 units, I want to go on to one marketplace, we're all properties are centralized in a unified, and normalized manner. Because that's the second point of it, is you have to be able to normalize expenses, if you want to start comparing apples with apples, and oranges with oranges. So that's the second phase. So what we're doing with MREX is we're building a unified, standardized marketplace for multifamily investors, where they will be able to see every single property that exists, that is for sale, despite on the way it's being sold or listed or marketed. We're going to be working with brokers obviously, the goal is not to get rid of brokers or anything like that, that's not, that's not what our goal is. Our goal is to help brokers, help investors just make the whole transaction process much quicker and more time efficient. And that way, you know, we're making the market more, you know, just a more efficient market.   James: Okay, okay. Got it. Got it. So you are basically streaming lining the whole selling and buying process, I guess, just to make --?   Nikolaï: Absolutely. Absolutely.   James: Okay, got it.   Nikolaï: And the analysis process as you said too, right, because it's one, it's one thing finding the properties and having them all in one marketplace. Okay, let's say, let's say you have the NASDAQ, let's say I wanted Lesson TechStars rather than multifamily properties. I go the NASDAQ and I can see every single company, I could have access to inventory, now that's the first step. Now the second step is, once you have access to inventory, and the information provided on all that inventory is normalized and standardize, well, I still have to be able to start comparing and start, you know, building my own models to say, well, if I'm a cash flow investor, which stocks are generating the most cash flow relative to the other, to the rest of the inventory. So that's where you know, context and alternative data comes into play with our platform, is that we want to be able to, to offer data and tools to you as a multifamily investor, to help you streamline your underwriting of the inventory that you've seen. So that's really the two things we're focused on at the moment.   James: Okay, got it. Got it. So interesting. So that'll be, that'll make a lot of, I mean, for investors or for buyers, they would be able to see what kind of deals that they want to buy,--   Nikolaï: Right.   James: Not just what they want to get the yield out of --   Nikolaï: Exactly and instead of going on fifteen websites, well, they've only one website, instead of having to, you know, start normalizing expense ratios and sifting through, through T-12 and T-3, and doing all that, it already kind of be all chewed up and kind of built up already. So you can actually focus, focus on analyzing, focus on comparing and establish, okay, I want to buy this property using this strategy. And why would I do that versus the other property that I see over there? That's ultimately what's the most important thing.   James: Okay, okay. So could it then be a good idea to match this with a crowdfunding platform, because during the crowdfunding, they can choose what deal they want, right?   Nikolaï: Right. So crowdfunding is an interesting thing. The problem is crowdfunding, obviously, crowdfunding, crowdfunding has tried to kind of attack two things. Number one is liquidity, right? Because, as a multifamily investor, the more properties that you acquire, you increase your net value, right, you're a richer person. But the problem with that, is that you have to leave equity in every single deal, right. The banks won't finance you 100%. So you always have to leave equity. So as you get richer and richer, value wise, you are actually cash poor, because you're leaving so much equity in each property that you acquire. And there's always a part of the equity that has to stay in those properties. But the problem, the second problem is that as you get, as you become a bigger investor, and you acquire more properties, and you're more well known in the market, well, you get access to better deals, but now you have less access to more money, even though you're richer. That's kind of the liquidity conundrum of multifamily investors. So that's why crowdfunding is interesting, because it gives kind of, you know, after the JOBS Act, it helps multifamily investors, particularly syndicators, to go and raise capital from, you know, from investors either through the regulation CF, you know, and obviously, regulation D506C was quite an upgrade also to be able to start to, to market capital raises. But what we're doing is we're actually building a second platform that is shadowing the Emirates platform. And what that platform will be doing is, we're actually going to create a sort of stock market and take the crowdfunding thing a bit further, because crowdfunding, as I said, tries to attack the liquidity conundrum. But the problem is, is that when you invest in a crowdfunding deal, you as an LP, are stuck in that deal for the lifetime of the deal. So if it's a five, it's a three to five year exit, well, your money stuck in that, so you, you as a passive investor, or as an LP, do not have liquidity. That's, that's one problem. And obviously, crowdfunding also helps with accessibility, right. So obviously, regulation D506C is only for accredited investors, which doesn't really help accessibility that much. Regulation CF has helped that because now then, that kind of lowers the barrier to entry for everyday retail investors who don't have that much money, but it's still a fairly limited regulation. At the moment, I know, they're trying to pass a couple of bills to increase the opportunity for regulation CF investors. So what we're doing is we're building a second platform, that's going to be basically a stock market, in its own sense, where, you know, through a broker-dealer partner that we hope to get. And then also through eventually a, an ATS license with the SEC, we would like to be able to take it a step further, and allow a multifamily investor to pretty much offer his property through one the various regulations on that marketplace. That way people could invest as passive investors, as LPs, either through Reg D, Reg CF, or eventually maybe even Reg A plus, but then they would also be able to acquire or access a secondary trading market so that they're not stuck in an illiquid period of three to five years. They would actually eventually be able to re trade part of their shares or all of their shares, kind of like you would at the stock market.   James: Wow. So it looks like you are trying to really disrupt the industry.   Nikolaï: Yeah, definitely. [inaudible 00:12:36]. You know, multifamily real estate looks like the stock market before the arrival of NASDAQ. Right? It's like before the internet, even though we have internet and multifamily real estate, it's as if people are still trading kind of like stock market investors were trading on floors, you know, with papers and screaming and doing all that stuff. It, you know, it doesn't make sense.   James: Yeah, yeah. It's so private nowadays, right? I mean, everybody has priority, we do not know how, even multi families performing under a different private LLC.   Nikolaï: Exactly.   James: There's a lot of good news out there. But there's also bad news, but nobody talks about it. right. So I think,--   Nikolaï: Oh, right. And the data, the data out there, like look at any of the data from, you know, even from the really big organization like NCREIF so the National Council of Real Estate Investment Trusts, NCREIT sorry. Even their data, when they know these indexes based on multifamily markets is based on a very low volume of the actual number of transactions. So when say a, a company, various data company says, well, the cap rate right now of say Atlanta is 5%, for example, well, that's actually based on a very small portion of overall transactions. So it's hard for us as multifamily investors, to really be sure are about the numbers that we're inputting into our underwriting models, because we're basing it off so little data.   James: Got it. Got it. Yeah, it's, it is just so limited, right? Because everything is done on a private basis on syndication, which is not much of the data being published out there, right. So --   Nikolaï: It's like investing in the stock market, but not knowing how the stocks have performed historically.   James: Yeah. Correct. Correct. So but why do you think this would work? And because if you look at the demographics of the, I mean, because I'm looking at syndication, when we whenever we buy for multifamily.   Nikolaï: Right.   James: But for me, it's just a small part of the whole market.   Nikolaï: Right.   James: Even though we are I mean, maybe my group or my network thinks that that's the whole thing how people buy multifamily. I don't know, that's true, because I network with a lot of different type of people, right. So looking at the classes of investors who are buying multifamily, I think I know for me, my thing is maybe we are one of the, I am one the lowest level part of it, right, because we are buying Class B and C using high net worth individuals and all that, but there are a lot of higher network, higher calibre people who are playing at a different level, which we don't have, which I don't have visibility, maybe you have it right so. So are you trying to look at different classes of investors and cut through all of them? Are you looking at only some classes of people?   Nikolaï: So we're trying to help what we call the small cap to mid middle market investors.   James: Okay.   Nikolaï: So anyone who owns between five units and about, you know, I'd say around 2500 to 5000 units.   James: Okay.   Nikolaï: That's kind of where we stopped, you know, that's where we're focusing on because that, you know, the majority of transactions are actually done by, by small cap to mid-market investors.   James: Okay.   Nikolaï: You know, the multifamily market is historically a mom and pop market. Now, it's, you know, it has transition a bit, investors are getting bigger and bigger. But the reality is the majority of the market is not an institutional market, you know, at the root level, or the private equity firm level or family office level, depending obviously, which metro area you're in, right. New York City is obviously more of an institutional market. Canada, Toronto is a very institutional market, but the majority of cities and metro areas are still, you know, very small cap market. And the problem is that, you know, take you for an example as a syndicator, or even take someone who's not a syndicator, right, because a lot of investors, multifamily aren't syndicators, they just buy their own properties, you know, they end up with maybe, you know, anywhere between 50 and 500 units as time goes by. Now, the problem with with those types of investors and syndicators as yourself is that you do not have access to a team of underwriters, you don't have access to, you know, expensive data that say a real estate investment trust has more than a very big private equity firm has, you don't have access to all those analysts. So, you know, we want to try and make sure that the market stays very level and stays is a level playing field. Because, you know, ultimately, I think the multifamily real estate market is very important for a couple of reasons. Number one, you know, everyone talks about the disparity of wealth, right of the 1%, and how the disparity is getting bigger and bigger. And we could do a whole podcast on that and why it's happened and where it's kind of going. But ultimately, I think, you know, the multifamily market is probably, the market, it's probably the asset class that offers the best returns based on risk, with the best risk-adjusted returns. If you look at Sharpe ratios, and Sortino ratios and all these things. Now, it's also been proven, there's a lot of studies about this, a lot of university studies done on this, that, you know, social mobility comes from education, and access to property, right. The reason why people have been so poor for so long, and like the Brazilian favelas, or the Indian shanty towns, is because people don't have education, and they do not have access to property, they are not able to become landowners, or owners of their own homes, even less become investment property owners, right. So I think multifamily stays as a very important asset class, because, on top of filling a basic need of human beings, that means providing somewhere to live, it also is a very important mover, for the everyday investor, the mom and pop, just the normal person need you to be able to access a very good, very safe, wealth building asset class that does not have the same volatility, or the same pitfalls as say, the stock market and other types of asset classes. So I think it's very important that we provide, you know, tools and data and allow for the smaller investor, the investor that has less than 1000, or even less than 5000 units to be able to continue on performing, continue on from this, this asset class.   James: Got it. Got it. So let's go to a bit more details on some of the big data and artificial intelligence, right.   Nikolaï: Yeah.   James: So yeah, I studied artificial intelligence almost 24 years ago, every now it has become really popular, a lot of startups with artificial intelligence, right.   Nikolaï: Absolutely.   James: So the question is, how do you, I mean, first of all, let's define what, can you define artificial intelligence in your terms in terms of real estate? Because I studied engineering standpoint.   Nikolaï: Yeah, well, I'm not an engineer, by trade, so at least I'll give more of a generalist definition to the people listening which I think is probably gonna be very good. The important thing is to understand, kind of the difference between machine learning and artificial intelligence. So you know, machine learning is more of a, it's a less automated process, right. So a lot of what people are calling artificial intelligence is ultimately just machine learning. And what it is, is that let's say, let's say, you know, I'm a data scientist or an economist, and I build a predictive model using, say, Monte Carlo simulations. Well, I set a, I build a set of hypotheses, I plugged them into my Monte Carlo simulation, and then that runs. Now, with machine learning and artificial intelligence, what becomes very fun as you know, statistics are a funny thing, right? And economic modeling is a very funny thing because even though, you know, people in the economics world swear by predictive analytics, the reality is in data science, it's garbage in garbage out, right. So the outputs always depend on the inputs. So let's say you're doing an underwriting model, and you're looking at an apartment building, and and you say, well if I buy this apartment build in this way, my internal rate of return is going to be 25%. Okay. Now, internal rate of return, net present value is a, is an output or their outputs based ultimately on the strength of those outputs are only as good as the strength of the inputs.   James: Correct.   Nikolaï: And the very important inputs that affect an IRR and NPV, which ultimately led to two of the most important metrics to help you decide whether it's a buy a property or not are rent growth, expense inflation, refinancing interest rate; if your IRR and NPV is based on on refinance, because obviously IRR and NPV has to be based on an exit model. And the exit model can either be a refi or it can be a sale; disposition. And then if it's a disposition, while your IRR and NPV is based, ultimately off the reverse, the reversion cap rates, so the exit cap rate upon sale. Now what everyone's doing right now, in the multifamily market, especially small investors, and mid-market investors is they're just entering these inputs. You know, they're just playing it by ear, and they're not even playing it by ear. They're coming up with these random inputs that are based off absolutely nothing. I just had a huge discussion on LinkedIn about this, with a couple of investors where one guy was saying, well, you know, if I buy it at 5% cap rate, my underwriting model, what I do is, to establish the reversion cap rate. So the cap rate upon eventual sale, let's say five years, is I add 20 basis points to the purchase cap rate per year. So if I bought it at five today at a 5% cap rate, well, then five years from now, I predict that I'll sell it as 6% cap rate, okay. And, you know, people kind of hide behind this type of rule of thumb model, say, well, I'm being conservative, therefore, my underwriting models very good. The reality of it is your underwriting model is bullshit. Okay. It's not worth the the Excel spreadsheet that it's been written upon. The reality is, where are you pulling this, this expansion of 10% or 20%,10 or 20 basis points per year? What are you basing that off? Right? That's what anyone should be asking, What are you basing this off? While being conservative. How do you know you're being conservative?   James: Yeah.   Nikolaï: How do you know you're not being optimistic? Right? You could be being you could actually be very optimistic with that. And conservative might be and then an increase of 0.25 a year, right? The reality of it is that everyone underwriting deals, right now, they're not basing their inputs off any data, right. And they're definitely not basing it off any predictive analytics, because it's one thing to have the data, the historical data. But you know, just because you have historical data doesn't mean necessarily, that's going to repeat itself in the future. That's why we have predictive analytics. So let's say that based on historical data, your 5% acquisition cap rates will actually be a 5.5 in five years. Now, the problem with that is that the future, that history is never guaranteed of the future, right. So that's why you then have to plug in various scenarios where you're considering this. And that's where predictive analytics come very difficult because you're pretty much just kind of taking a shot in the dark and basing things off the past, but you're putting in like a margin of error. With machine learning and artificial intelligence, you're able to make your predictive models better ex post based on ex ante results. So let's say you create a model to predict the future cap rates, well, you want to predict the future cap rate of in five years, it's your goals to sell within five years. Well, if you predict that today, the probability that your five-year cap rate from now is going to be precise, is a lot lower than let's say, in four years, you predict the cap that same cap rate, right, because you'll be closer to your exit. So there'll be less room for margin of error. So what machine learning and artificial intelligence will allow you to do is to consistently kind of reset your model as time advances. So maybe your initial model based upon acquisition was off. But as you advance in time, the artificial intelligence and machine learning continues on training that same model, the same algorithm that you had, and adapts the various inputs and algorithms to make it more and more precise as you get, as you get closer. And on top of that, as you get closer, the range of distribution of property probabilities get smaller. So it's a double effect, your predictive models get even tighter and tighter as time goes by. And that's where [inaudible00:26:03] machine learning and artificial intelligence can really help out. Is that instead of just plugging in these ridiculous exit cap rates, and ridiculous growth rates and ridiculous inflation of expenses, and absolutely ridiculous refinancing interest rates, when we get closer and closer to being able to actually put in inputs that are based on something very, very solid and then, therefore, our underwriting models will become more and more precise. And what we want in underwriting when you're buying a property, whether you're a syndicator, and you're responsible for money of your LPs, or whether it's your own money, the goal of underwriting is not to be conservative. That's not what the goal of underwriting is. And anyone who says that they underwrite, and they're concerned, their underwriting is conservative, what they're really telling you is they don't know how to underwrite, okay.   James: Yeah.   Nikolaï: You don't want to be conservative, you want to be right on the dot, that's what you want to do with underwriting, you want to be as precise as possible because the reason that you buy the property today is you buy it for future cash flows. And cash flows can come in various ways, they come in an annualized cash flow so, so free cash flow, they come in the appreciation of the asset, so the value of that asset gains because of various market dynamics and because of the way you're, you're managing that property. And they also come through the capitalization of your mortgage. So there's a part of your mortgage that you're paying down, which is principal, right. So those are the three cash flows that you can receive. Now, when you're underwriting a deal, and you're looking at how much you should pay for, say, this hundred unit building you're looking at, well, if your inputs are off, you might buy that property. But it's a bad acquisition because you were too optimistic in your inputs. But it also happens that you were too conservative in your books, therefore, you didn't buy the property. Because if you input that at the exit capital, that property is 7%, but, in reality, five years from now, the exit cap rate is five and three quarters, well guess what? You missed one hell of an opportunity.   James: Correct.   Nikolaï: And in real estate investing, the most important thing is time value of money, we only have a very limited time during our lifetimes in which we can invest and create wealth. And we only have so many hours during the day. Therefore the cost of opportunity, the time value of money are the things that we should consider the most in our underwrite. And that's really where machine learning and artificial intelligence will help investors become much, much better. Obviously, you also need education, right? You have to understand these, I mean, this is advanced stuff. And I'm trying to kind of explain it in a simple way, where people who don't have master's degrees and PhDs in finance and engineering can understand it. But the reality of the matter is that multifamily investing is very, it's a very complex, it's a very sophisticated asset class, and you need a certain level of education.The problem being right now, despite the very high level of education that some investors have, we just don't have solid, predictive analytics tools and data to be able to make sure that we're actually able to transfer education into decent acquisitions.   James: Yeah. Well, that's very interesting, because exit cap rate is always being misused or mis-conservative right? So --   Nikolaï: Well, even entering cap rates, even acquisition cap rates, I see people saying, well, you know, I'm not gonna buy that property because it's a five cap rate and the markets trading at 5.5. Okay, is that a stabilized property? No, it's a value add property. Well, the cap rate doesn't, the cap rate is meaningless then. A cap rate is a metric of a stabilized asset. If the asset is not stabilized, there is no cap rate, because a cap rate is a perpetual annuity. It's a return metric, based on an unlevel perpetual annuity, which means the same cash flow every year forever.   James: Correct.   Nikolaï: Now, if you want to be able to calculate that your property has to be stabilized. So if you're not buying a property, because it's a five cap rate, and the market sharing at 5.5, but it's a value add deal, well, I'm sorry, I'm sorry to tell you, you should change, you should change fields, you should go play, you should go to Las Vegas and put it on red.   James: Not only that, I mean, not only new investors don't understand the entry cap rate doesn't matter [inaudible 00:30:46] and I don't know, I never see a reason not to do a stabilized deal. Not on commercial, right? So for me, I'm always [inaudible00:30:53] guy, that's why I --   Nikolaï: Well, unless you're a private equity firm or your family office or you're a RET or you're an ultra high net worth individual who now has, you know, net value of anywhere between ten and hundred and fifty million dollars, there's no real reason to do stabilize deals, right. The reason you wanted to stabilize deals is, because you have a very high net worth, or because you're trying to de-risk your portfolio. Right?   James: Correct.   Nikolaï: That's why you would just stabilize deals for small cap or mid cap investor.   James: Yeah, yeah. Most of the time. I mean, commercials always value at play. I mean,   Nikolaï: Of course.   James: I mean, there's a lot of people doing stabilized deal nowadays, just by getting a higher mortgage and getting slightly lower price, play on the mortgage side with the interest to get a cash flow, but --   Nikolaï: And that can work if you're a neurosurgeon, right? If you're a surgeon making a million and a half a year, and you're 35 and you say, well, you know, I want to start buying multifamily property because I like, I like real estate and I like the tangible part of the asset class. But I don't need any money right now, because I'm making a million, I'm making a million and a half a year. I don't need any cash flow. And I'm very long term and I just want to build myself a nice retirement, you know, because you know, that's what I want as objective. Well, then yes, buy stabilize property or be an LP and syndication, or purchase that stock in the [inaudible00:32:23], that's fine. But if your goal is to increase your wealth exponentially, in a short period of time, and what I mean by a short period of time is fifteen to, five to fifteen years. Well, then, yeah, you're gonna have to do some kind of value add, you can't just do financial arbitrage all the time.   James: Yeah. Yeah, there's a lot of deals out there in different asset class, which can give you that cash flow, right. I mean, you can buy a stabilized mobile home park, you know, it'll give you higher cash in cash than any multifamily deals.   Nikolaï: Right.   James: So even self-storage, or even multifamily, which has been stabilized, you get, you'll get good cash flow. But how long will that cash be guaranteed? Because you have a very tight DSER at that point of time. And let's say the market turn, you may not be, your DSER might be compromised right now, because you don't have any buffer. Right?   Nikolaï: Especially if you did not properly manage the terms of your mortgages. Right. So that's very dangerous. Like if you feel that you're, if you feel that the markets going to shift, say interest rate wise, the easiest way to kind of pull yourself out of that situation you just talk about is, you know, just take longer-term mortgages, you know, make sure that the mortgage does not end in five years, make sure it's a 10 year term, or even maybe a 30 year term. Right? That's, that's the easiest way to manage that risk.   James: Yeah, just do a hard loan.   Nikolaï: Right.   James: Which gives you like, 45 years. I mean, there's the other trick that a lot of people play is, you know, showing you need cash in cash based during IO period. And nowadays, people are getting five years, seven years, IO period and sometimes people think, oh, I will not hold, you know, that deal for long term. I mean, you are hoping on not holding, holding, right. But you do not know what's going to be happening to the economy, right?   Nikolaï: It's a dangerous game to play. And I'm not saying don't play it, but make sure you have the, make sure you have the education and the know-how to be able to manage that risk. It's all risk management. Ultimately, that's what it is.   James: Yeah, yeah.   Nikolaï: The problem, the problem is a lot of people are doing this, and they don't know what the hell they're doing.   James: Yeah, I mean, I think so there's so much of capital out there right now, looking for money to be placed in some way.   Nikolaï: Oh definitely.   James: And people don't think that are they going to putting 1% in the CD, I might as well put here and get like six, seven per cent, right? Cash Flow, right? And,--   Nikolaï: And that's, that's the retail market. Like that's, that's small investors like me and you the reality of is the real cap, the real capital flow right now is at the institutional level, there is so much higher level money and smart money searching for returns right now. I mean, we can't even fathom small investors, how much money, I mean, family offices, typically, if you take the family office market, typically always allocated maybe like, I don't know, depending on the family office in the region, but usually anywhere between, you know, maybe eight to twelve per cent of their overall asset allocation, capital allocation to what they call alternative assets, right. And real estate as part of alternative assets. Now, over the last 10, I'd say over the last 10 years, the last decade, family offices have become more and more in tune to the real estate markets. High net worth families also, especially towards like multifamily real estate, and more and more real estate is no longer considered just as, as something under the alternative asset umbrella. But now it's kind of becoming its own umbrella. And what that's doing is that instead of family offices, and we're talking about family offices that have trillions of dollars, right. These are not these are not small things, these are big moving bodies with a lot of capital, we're talking about multi-billions of dollars, not trillions, multi-billion dollar family offices, that are now instead of allocating, you know, 8% to real estate, well, now they're allocating 20% to real estate. So and that's, that's a scale like, there's a lot of them out there. And we haven't even talked about the private equity firms. We haven't even talked about the pension funds, the International pension funds, you know, people talking about globalization and international money, thinking that it's just, you know, rich Russians is going to Sunny Isles, Florida, buy $10 million condominiums. That's not what it is. The global movement of money to American and Canadian Real Estate are things like the Amsterdam teachers pension fund, or government workers pension fund, you know, allocating, allocating, you know, 100 billion dollars to the American real estate market. Now that's, that has a big, that puts a big dent on the supply and demand of real estate. And that's what ultimately drives property value is much more than interest rates. Interest rates only, only influence property values, like people were talking about, especially the last couple of years, all we know, if interest rates go up, cap rates will follow up, they'll go up. That's not true. Capital flow drives cap rates and values and properties and multifamily; interest rates only influence cap rates and values.   James: Very interesting perspective, that's you are right. There's so many, too much money, even out of United States is looking for money to place, right. Like the other dad had a call from the UK. It's a family office who want to invest in the UK and they're looking for like operators like me, and I was asking them, what's the return expectation? They say this 22% IRR credits and I said, well, I [inaudible 00:37:58] you guys, I can get better money in the United States right, so --   Nikolaï: Exactly. And all the, all the money from the quantitative easing the follow the 2008 crash, I mean, all that quantitative easing money, a lot of it still, after even 10 years, has not even found a place for it yet. Right? So there, there's a lot of money chasing deals, there's a lot of money chasing deals.   James: Correct. Correct. Right. That's true. That's true. So coming back to the exit cap rate. So I know that's one of the hardest parameters to measure. Right? So.   Nikolaï: Absolutely.   James: But can you clarify again, how did you, how would you use artificial intelligence to find that a more accurate exit cap rate? You know, T minus five, my T minus 5, five years earlier, before you hit that five years mark of selling, assuming five years of selling.   Nikolaï: So it's the computing power, right. So it's a computer, what we do is, we'll build, so we'll do we'll say, I'm sorry for anyone who hasn't studied, you know, high level university finance, but or statistics, you know, we'll build a, say, a regression model. So we'll look at past data. We'll plug all that in, in order to build a predictive model, a future model being able to come out with future cap rates, and, you know, the more data that we're able to plug into our regression model. So historically, what real estate institutions and economists have use is what they call the linear regression model, use the Monte Carlo simulations. Now, the problem with the linear regression model is that you know, past transactions or data are, are, are also affected a lot by various things like, you know, political environment, and capital markets. And there's a whole bunch of factors. So there's a new model that's being used more and more, especially with a lot of postdoctoral students in statistics, it's called a Quantile regression model. So that's where we're able to create that same kind of, I'm saying this in layman's terms as much as possible, we're able to take past historical data, build that kind of linear model, kind of, like build that line chart for people to understand, and we kind of repeat that line chart in the future. But we're also able to start to weigh that those data points with various things like a new government, with quantitative easing, with the war, with various factors that may be affected that models to make it less linear. And then we're able to start to better predict future stats and future cap rates. So that's the first step of it. The second step is, let's say, right now, we built our Quantile regression model. And now we compute it and what it says to us is well, T minus five cap rates, or five-year cap rate is going to be between, let's say, we have a couple of tracks, it's hard to explain to people who have not done statistics. But we have a couple of tracks. And ultimately, what it says is that the highest probabilities are that cap rate is going to be between 5.75 and 6.10% in five years for that specific market. Now, like I said, as we get closer to the five year period from now, the less the margin of error is, because we're closer and multifamily market moves very slowly. So predicting, the easiest way to understand is predicting 25 years out from now, it's very hard? Your 25 year prediction is going to be way more, there's more room for it to be completely off than your two-year prediction. So we build a model for the five-year prediction, and then starting tomorrow, every day, our artificial intelligence recalculates that model. So as it recalculates, the model gets more and more precise, because let's say we took statistics from today to 20 years ago, let's say we took the cap rate of that market, starting from today, and 20 years back. Well, obviously, the next 20 years are not going to be exactly the last 20 years. But that's ultimately what statistics do, we try and kind of say, well, let's take the last 20 years, there's a margin of error, that's what's going to be the next 20 years.   So what's cool with the artificial intelligence is without actually having to do anything, every day, the artificial intelligence kind of brings the model a day closer and adapts the model with more and more weight on what's going on right now, rather than what happened 20 years ago. And the artificial intelligence is also able to measure what today it predicted for yesterday, versus what actually happened. And what's the spreading difference and what caused that spread? And therefore, once it's able to determine what caused that spread, it'll add that into the equation for the future cap rate model so it becomes much more precise.   James: Yes, but don't try to run it in iteration on a daily or monthly basis to watch the whole investment process. But how do you make it on day zero? Well, today we're buying today how does it iterate then when on a day zero?   Nikolai: Well, what it is I don't understand the question.   James: So my question is, you said the data is being fed into the system to get more accurate exit cap rate. But you're making a decision to buy today? Is the iteration happening from today to all the investment cycle? Or do you do it earlier before you decide to buy a deal?   Nikolai: Okay, I understand what you mean. So like, for determining your actual purchase cap rate,   James: Yes, correct whatever price that I'm going to pay today because that's what I'm getting into the deal. That's the point of me making a decision, whether this is a good deal, and I'm going to be raising money and telling everybody it's a good deal.   Nikolai: The purchase cap rate is a whole other set of statistics and data models. That's more I'd say, determining today's cap rate is much more endeavor of collecting more historical data. Because like I said, let's say JLL Jones Lang LaSalle which is one of the biggest brokerages, they come out with reports and say, Okay, well, the cap rate, let's say in Austin is, 5.2%. Let's say the mean cap rate is 5.2%. Well, that's based on maybe what like 30 or 40%, of actual transactions that happen because they don't have data on like the off-market transactions, or the pocket listings or this and that, right. And on top of that, they haven't normalized the cap rates on whether, let's say, a building traded at a 4.6 cap rate. Well, as we said, if that property wasn't stabilized, well, then that cap rate is off. That's not a good cap rate. So that's a second thing. So for establishing what you should pay to the intrinsic, what's intrinsic value today. that's ultimately what I think the question is, and correct me if I'm wrong, but let's say you're looking at a 100 unit property, what is the actual intrinsic value of that property? What's the real capital I should be buying at? Well, that's a question of having the proper volume of data, Okay, number one. So that's what we're working on right now is making sure we keep on building our database. So instead of our market cap rates being based on the off 30 or 40%, of inventory, or transactions. Well, it'll be based off maybe 60, 70, 75%, therefore, that cap rate becomes more precise. Secondly, we actually look at every transaction and say, qualitatively because that's the first thing is a quantitative aspect, in statistics, we have quantitative, qualitative. So the quality of the data, once we have the quantity, we look at the cap rates and say, okay, that property traded for a 4.2 cap rate. Was that a stabilized property? No, it was not. Once we add the cap x, we have the new revenues. And we adjust the sales price for cap x, but we also adjust NOI. Now we can look at the stabilized cap rate. So that's the qualitative aspects of it. And now we're able to say, here are the market cap rates, here's the low end of cap rates, here's the high end of cap rates, here's the mean, or the media. And here's that range of cap rates. Because cap rates are based on the Capri calculation ultimately, even though people think it's NOI divided by sale price, I'm sure that's not what a cap rate is, that's how you find the cap rate of a soul stabilized property. The actual cap rate calculation or formula is a mathematical equation of R minus G, it's algebra, so are being returned minus g, which is growth. And R is defined as RF plus RP. So the risk-free rate plus the risk premium that you as an investor are looking for or that the market is looking for, a perceived risk premium, obviously. So what we want to do then, that would be like a third step, and we're not at that level right now. But I hope within the next couple of years, we will be, and I'm sure you as an engineer, probably understanding how valuable our ability to do that would become for the market. Is that then you're starting to be able to say, well, right now, that property is being listed at a say, let's say the range for cap rates in Austin is really five to six, obviously, six is going to be in the worst neighborhoods. Five is going to be the best neighborhoods because it's a matter of risk. Well, then you're looking at the property, let's say it's at a 5.7 cap rate. But it's kind of on the limit of a bad neighborhood, good neighborhood. And then you're able to intrinsically say, but the intrinsic cap rate of that property, the real intrinsic value of that cap rate is actually 5.3. Now, if you didn't know that, and you just said, well, the average cap rate is 5.7 well, it's not so much of a deal, I'm not gonna buy that property. But now with this new data, what you're able to see is, wait a minute, it looks more expensive than what it should be but in reality it's not, it's actually cheaper because the real intrinsic value is a 5.3 cap rate. And that would really unlock the potential of what we call value investing, what like a Warren Buffett has built his entire career off of the stock market? Well, he was able to build that value investing exists so much, in the stock market, because of the quantity and the quality of the data. The quantity of data is accessible to everyone, the quality of the data is a bit harder to get the qualitative aspects. That's why Warren Buffett was has been such a great investor, because he invested so heavily into being able to pull out the qualitative aspects of the data, well, now we would be able to do the same thing, you would be able to do the same thing as a multifamily investor. You would have access to the quantity of data needed for you, then to increase your knowledge based on the qualitative aspects of it, and then be able to properly price that acquisition. And then once you're able to do that, well, then you can go say to your investors, look, this is why I'm buying this deal. This is why it's a good deal. And if on top of that, you're able to be more precise with your exit cap rate, and the growth rates of your revenues and expenses and your refinancing rates. Well, you're going to be a much more confident investor.   James: You are making it really what you call a --   Nikolai: It's a more efficient market.   James: It's a more efficient way of actually determining your purchase because you can really just say generally, Austin is what five cap, it's not true, [inaudible00:50:46].   Nikolai: It's kind of scary to say, but we're all kind of invested in multifamily kind of half blindfold. The guys like me and you, and there's a whole bunch of other guys out there really intelligent wrestlers. We're all invested, based on intuition experience, a very strong knowledge base. But we're ultimately kind of invested with one eye closed. Now it's even worse for people who don't have our knowledge base and experience because they're all invested in completely blindfolded.   James: Interesting. So, if you can get that kind of data where you can look at the stock market, and what's the potential, especially if it's in the path of growth. And what's the risk that you're buying? There are some deals, even though you buy it at the lowest cap rate for that market, it could be still the best growth because it could be just like another big explosion, in terms of jobs, is going to be happening in that area just because of the path of growth.   Nikolai: That's so important because if you're a pro forma and you're underwriting you predicted a 2% growth rate in revenue. But in those five years, the analyze growth radio was six. Well, you probably didn't buy that property, when you should have. And the other thing is the same if you predicted a 6% growth rate, and it was two, then you bought that property you shouldn't have, But what most people will say is well, the guy who predicted 6%, he should have put in 2%, like he should have been conservative, but that's not necessarily true. That's a half-truth. That's actually a mistake in logical reasoning because the other guy who says, I'm going to plug in a 2% growth rate because that's what historically happens. What happens if you invest in a market where the growth rate is actually 6%? And that the other intelligent investors knew or predicted that it would be 6%, while they're willing to overpay, according to you for a property, and then you're not buying anything, you're not generating any returns, you're not building your wealth, and you're just kind of sitting on the sidelines there, Bah, humbugging saying, well, the markets paying way too much for the properties and these guys are stupid, stupid money, blah, blah, blah, I'm going to wait for the market to crash and blah, blah, blah, I know guys who've been saying this since 2012. And they have not bought anything since 2012. They haven't generated any returns. All under the pretext of being conservative investors. You know what, they're not conservative investors, you know why because they're not investors. They haven't bought anything, because they take themselves out of the market, and they're sitting on the sidelines, and they're just making up for lack of precision in their underwriting through, this kind of pseudo-conservatism.   James: I think it just depends on the sophistication of the investors. If you look at nowadays, multifamily has become so popular, so many people who did not have the financial education background or the way to analyze a deal. There's a lot of parameters that go into any deals. That's what you mentioned, you mentioned so many parameters, nobody will look at that. Everybody said multifamily is good. I bought it and it went 300%. And they say, Oh, I'm a really good operator. Well, actually, you should have made 500% because the market gave you at least 400%. 100%, you just did 300%, why did you do 300%?   Nikolai: That comes down to what we call the search for alpha. We want to outperform the market. And all these people and there's a whole bunch of them now there's gurus and mentors and coaches, and they're giving all these online classes or seminars or whatnot, or they're boasting about being such great real estate investors. And the reality of it is they don't even know what they did. They're like, well, I generated X percent returns, and I've created X amount of millions of dollars in profit over the last five and 10 years. But that's actually quite average. That's what the market does, as long as you are in the market. Of course, that's what you generated. Now, did you generate more than what the market did? That's the real question. And unfortunately, there are not enough people in the market asking that question. And if you're a passive investor, that's the question you should be asking your syndicator or your GP is not this is what you generated, great. That sounds awesome. You generated 22% IRR annually over the last five years. What did the market generate? The market generated 23.   James: I remember the other day I saw someone, he said, I made 60%. In one year, I bought it in the first year and I sold it in twelve months, I made 60%, I said well, you should have made that 100% because the market went up by that much.   Nikolai: And that's why I'm so bullish on education, and why I think it's so important that multifamily investors get educated and push their knowledge base, because, this is not Nintendo, this is not Xbox, we're not just playing, baseball on our PlayStation three, or Playstation four, this is serious business, and even more, so if you're syndicator. Just in the knowledge base, you know needs to continuously be expanded. And that's why data also needs to be there because knowledge without data is also quite useless.   James: Correct. So coming back to being the alpha in the market. I know you can look at different market appreciation versus how much you are making money. So coming to, let's say, for a decision where you have a deal in your hand, and you're deciding whether you want to sell or you want to refile, or you 10:31 exchange. So can you give us a good methodology to do to make that decision?   Nikolai: To make the decision on whether you beat the market or...   James: Whether you want to sell a deal, or whether you want to refinance, whether you want to hold it for long term or you want to do a 10:31 exchange? How would you approach it?   Nikolai: Well, I'd approach it on a very individual basis. Number one, I think everyone has a very different investor profile. What I mean by investor profile is, what type of returns do you want? And when? What are the strengths and weaknesses that you possess as either an owner-operator or syndicator or whatnot? What access to capital do you have? How patient is that capital? What's the cost of the capital? Now, if it's your own money, obviously, it's probably the most patient money with the cheapest cost of capital. If you're raising money from other people, well, then obviously, there's a less patient aspect to it, and the cost of capital is going to be higher. If you're taking money from bridge loans, well, that's even worse. So if you're taking money from hard money lenders, well, then obviously, your cost of capital is going to be very, very high. So these are all things that you have to consider, you also have to consider where you are in your career with regards to what it is that you want to achieve, either as annual cash flow or just overall that value and what type of risk you're willing to accept.   So ultimately, you have to be able to answer those questions initially, to be able to decide on the strategies. Because ultimately, people in multifamily investing, what they do not understand is the difference between philosophy and strategies. Now, everyone should have their own investment philosophy, based on their investor profile. Now, once you have that philosophy, what you want to do is adapt your strategies according to where you are in the market, and where you are in your career. That's something that is very misunderstood. People say, I'm a buy and hold investor. We hear that a lot in multifamily. So ultimately, what you're saying that you do not have an investment philosophy, that you think you do. You think your philosophy is to buy and hold. But buy and hold is not a philosophy, it's a strategy. So what you're saying is, ultimately, you're investing all the time throughout the whole of your career, using just one strategy. That's very dangerous because let's say the exit point of that strategy eventually, say the day that you do have to sell upon retirement because even though you're buying a whole, you might not be a legacy buy and hold investor. What I mean by that is a legacy buy and hold investor is someone who's just going to pass down the properties to their children, upon death, or upon retirement, whereas most buy and hold investors, what they really need is, I'm going to buy and hold until my retirement, then I'll start selling off. Well, what happens if, during your retirement, you're in a trough of the market cycle. What if you're in that part of the market cycle, or you're at the bottom of it, that's a really bad time to sell? Well, that's the mistake of always investing using only one strategy. So what I would say is that you have to establish your philosophy, understand that your investor profile is going to change over time. And the market cycle moves through phases, there are different phases of the market cycle and your strategies, you have to be able to use different strategies at different phases of the cycle, and at different phases of your career as your profile changes, or adapts or morphs. And that's how you then establish well, with this property, should I buy it and hold it or should I sell it? Or should I just refinance it? What should I do? And I'll give you a very concrete answer. Once I've explained all this.   I have a student here because I do teach real estate investing courses. We actually built a college we call it The College of the Emmerich's. Now you don't have to, it's not college level education. But what we're saying is that from everyday multifamily investors, if you really want to learn college level stuff without having to go to college, well, we have a couple of courses that we teach you very high-level stuff, very concrete work. You still need coaching from coaches and mentors and all that stuff. We actually teach courses. So one of my students in these courses, he's a very successful real estate investor in Montreal, Canada, Montreal is the most important multifamily market in Canada. It's a very strong multifamily market, very competitive. Now he's up to about I guess, 150 units, all on his own, no outside money, no passive money. And he started having trouble refinancing out of his properties because what he was doing, it seems a very big value add investor. So he was using two strategies value added buy and hold. But he was erroneously thinking that value-added and buy and hold was his investment philosophy, which is not, those are two strategies that are part of the philosophy. So he came to me and he said, well, look, banks have now started to tighten their DSCR ratings, and their LTV, therefore, I'm buying a property at a billion dollars, and putting in $300,000 into it. And now the market value of that property is $2 million. But I'm not able to refine it $2 million, because of the banking standards, they're only allowing me to refine out of 1.6. So now, if they're letting you refine out at 1.6, on a 75%, LTV, what they're saying is when you have to leave in 25% of 1.6 plus $400,000, that's a lot of equity, that it is unable to pull out because he was doing too much of a good job at value add. And the capital markets, the banks are not able to follow market value, banks, especially in Canada, are much more conservative than in the US, but even in the US, there is a lot of people buying properties. And they're not able to refine the whole value, because their total loan dollars are blocked by either LTV or DSCR. What I call economic value, the economic value is not as high as market transaction value. Therefore, instead of leaving 25% of equity, you're leaving 25 plus, in this case, $400,000.00. Now that's where I said to him perfect, I looked at his portfolio, I said, well, you have to adapt your strategies, you have to change the strategies, you can no longer at this moment, use the buy and hold strategy, you have to use the fix and flip strategy.   Because you're too good at fixing value add. And you're not able to pull out as much equity as you used to be through refinancing. Therefore, now you have to seriously consider selling that property. Because you can go and get $2 million for other markets right now. So that's an extra $400,000. Because he was able only to refinance 1.6 out of it. So now he's able to get the full market value, pull that cash out, and he has access to a lot of opportunities. He has a really strong bird document work. So his cost of opportunity is very high. If he's leaving all that equity, in these properties that are all stabilized, he's making way more money by doing more value-add stuff. So he made the decision and now he holds zero properties. He sold all of his 140 units because that has allowed him to get more and more cash rich, with less and less money and equity and properties and gain access to more and more opportunities. And ultimately, his annual portfolio, the total return on investment is in the 40 to 70% IRR. Whereas while he was doing buy and hold his overall portfolio was only returned to him maybe 20% if you consider the weighted average return on investment. So that's how I would attack that. I know, that's a very long-winded answer.   James: I think that's the right answer. So I mean, the return on equity, which is date right now, I mean, on this deal. There's so much of dead equity not producing cash. And if your cost of capital, which is also equal to an opportunity outside is much higher, you might as well just cash that out by selling it off.   Nikolai: Because the refinancing is living you to a liquid.   James: Recently, I mean the banks have been more stringent on refine. So the last refine they did ask me to leave 5% my cash basis, which they never did in the past, things have changed. I think that's okay. That's how the banks work now.   Nikolai: It's okay. But the problem is that on a $15 million property, you know, that's two and a half million dollars less cash you have for the next acquisition.   James: Correct. I mean, it depends on what is the cost of capital outside plus how much you can pull out and how much your equity stuck on it. So, coming back to market cycles, because I think this is one thing that I want to ask you because I think you have studied with Dr. Glenn Mueller. So right now, if I look at the latest Q1 forecast for apartments in the hyper supply market. I don't know if that's something that you are aware or not, but...   Nikolai: Nationally?   James: Nationally yes it's not a local, but lots of markets are in it for supply. It's very, very few markets are in the expansion cycle. And even though they are in the expansion cycle, they are at the last stage of the expansion cycle. And all the markets that are on expansion cycle, or the market that recovered late like Las Vegas, Phoenix and a lot of Econo markets. So can you give an overview of what do you think the market is? And what would the strategy be for investors now?   Nikolai: Well, I think number one, I would say that I try not to look at national or macro market cycles. I think that's the first thing to consider. Because multifamily real estate is so hyperlocal. So I look much more at those markets, cycles of hyper supply and expansion and contraction, I look at more of like a metro area. So like you're in Austin, Texas, I look at Austin, I wouldn't really consider the multifamily market at large, because it's kind of like looking at cap rates on an unstabilize property, it's kind of a waste of time. Now, I'd say that I haven't looked at recent data of where all the cycle, where all the markets are, the phases of the cycle. But I mean, I think it is safe to say that, most of the markets right now are in the later phases of the game, or later innings, as Howard Marks likes to say, in the stock market and capital markets. But also, as he says, we don't really know, see the thing with market cycles, and whether it be with Dr. Mueller, whether it be with Karen Trice, out of Australia, and also all the other various professors and researchers of market cycles, is

Der Podcast "Einführung in die deskriptive Statistik" richtet sich an Studierende der Wirtschaftswissenschaften in den ersten Semestern.In der dritten Vorlesung des Wintersemesters 2011/12 behandelt Dr. Heumann folgende Themengebiete: Lagemaße; Modus; Median;Quantile;Boxplots;Mittelungen

In Folge 3 behandelt Dr. Heumann zunächst Themen des 3. Kapitels - Lagemaße: Modus, Median, Quantile, Mitteilungen, Box-Plot. Aus Kapitel 4 - Streuungsmaße: Spannweite; Quartilsabstand, Varianz.

Linear regression is a great tool if you want to make predictions about the mean value that an outcome will have given certain values for the inputs. But what if you want to predict the median? Or the 10th percentile? Or the 90th percentile. You need quantile regression, which is similar to ordinary least squares regression in some ways but with some really interesting twists that make it unique. This week, we’ll go over the concept of quantile regression, and also a bit about how it works and when you might use it. Relevant links: https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143 https://eng.uber.com/analyzing-experiment-outcomes/

Übergewicht und Adipositas ist ein weltweites Problem, das bereits im Kindesalter eintritt. Neben einer unausgewogenen Energiebilanz gibt es weitere Faktoren, die die Entwicklung des Kindes bereits im Mutterleib beeinflussen und das Risiko für späteres Übergewicht erhöhen. Eine kürzlich erschienene Arbeit hat gezeigt, dass 7% der Wahrscheinlichkeit im Alter zwischen 7 und 10 Jahren adipös zu sein, durch mütterliches Rauchen während der Schwangerschaft erklärt wird. Dieser Zusammenhang zwischen mütterlichem Rauchen in der Schwangerschaft und Übergewicht und Adipositas des Kindes wurde jedoch aufgrund von potentiellem Residual Confounding immer wieder in Frage gestellt. In der vorliegen Dissertation wurde untersucht, wann in der Kindheit der Zusammenhang des mütterlichen Rauchens in der Schwangerschaft und späterem Übergewicht erkennbar wird (longitudinale Quantilregression unter Zuhilfenahme der Boostingschätzmethode) und ob dieser Zusammenhang durch residuales Confounding erklärt werden könnte (negative control design). In den Ergebnissen zeigte sich, das höhere, weiterhin ansteigende BMI z-score Differenzen bei Kindern, deren Mütter in der Schwangerschaft geraucht haben, im Vergleich zu Kindern, deren Mütter nicht in der Schwangerschaft geraucht haben, im Mittel und Median ab einem Alter zwischen 4 und 6 Jahren eintreten. Diese Unterschiede wurden für die unteren und oberen BMI z-score Quantile in Abhängigkeit von Geschlecht und Alter gefunden. Des Weiteren wurde in einer Meta-Analyse die gepoolten, gegenseitig adjustierten Effekte des mütterlichen Rauchens denen des väterlichen Rauchens oder des Rauchens im Haushalt auf das kindliche Übergewicht und Adipositas gegenübergestellt und dabei ein höherer Effekt für das mütterliche Rauchen im Vergleich zum väterlichen Rauchen festgestellt. Dieses Ergebnis lässt einen direkten intrauterinen Dosis-Effekt des Nikotins vermuten, da Kinder beim aktiven Rauchen der Mutter stärker betroffen sind als beim Passivrauchen. Um dieses Ergebnis weiter zu bestärken oder einen eventuellen Schwellenwert zu erkennen, wäre der nächste Schritt, den Dosis-Effekt des Rauchens der Mutter mittels einer Individual Patient Data Meta-Analyse auf Linearität näher zu untersuchen.

Ragnar's adopted home is destroyed. Orcs are incoming. What are those pillars of light?

The adventurers face an approaching hoard of Orcs as they try to make their way to Quantile

Aue, A (UC Davis) Thursday 16 January 2014, 10:30-11:00

Quantile regression allows to model the complete conditional distribution of a response variable - expressed by its quantiles - depending on covariates, and thereby extends classical regression models which mainly address the conditional mean of a response variable. The present thesis introduces the generic model class of structured additive quantile regression. This model class combines quantile regression with a structured additive predictor and thereby enables a variety of covariate effects to be flexibly modelled. Among other components, the structured additive predictor comprises smooth non-linear effects of continuous covariates and individual-specific effects which are particularly important in longitudinal data settings. Furthermore, this thesis gives an extensive overview of existing approaches for parameter estimation in structured additive quantile regression models. These approaches are structured into distribution-free and distribution-based approaches as well as related model classes. Each approach is systematically discussed with regard to the four previously defined criteria, (i) which different components of the generic predictor can be estimated, (ii) which properties can be attributed to the estimators, (iii) if variable selection is possible, and, finally, (iv) if software is available for practical applications. The main methodological development of this thesis is a boosting algorithm which is presented as an alternative estimation approach for structured additive quantile regression. The discussion of this innovative approach with respect to the four criteria points out that quantile boosting involves great advantages regarding almost all criteria - in particular regarding variable selection. In addition, the results of several simulation studies provide a practical comparison of boosting with alternative estimation approaches. From the beginning of this thesis, the development of structured additive quantile regression is motivated by two relevant applications from the field of epidemiology: the investigation of risk factors for child undernutrition in India (by a cross-sectional study) and for child overweight and obesity in Germany (by a birth cohort study). In both applications, extreme quantiles of the response variables are modelled by structured additive quantile regression and estimated by quantile boosting. The results are described and discussed in detail.

Background: The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. Methods: We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. Results: The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Conclusions: Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.

Previous studies suggested potential priming effects of gestational weight gain (GWG) on offspring's body composition in later life. However, consistency of these effects in normal weight, overweight and obese mothers is less clear. We combined the individual data of three German cohorts and assessed associations of total and excessive GWG (as defined by criteria of the Institute of Medicine) with offspring's mean body mass index (BMI) standard deviation scores (SDS) and overweight at the age of 5-6 years (total: n = 6,254). Quantile regression was used to examine potentially different effects on different parts of the BMI SDS distribution. All models were adjusted for birth weight, maternal age and maternal smoking during pregnancy and stratified by maternal pre-pregnancy weight status. In adjusted models, positive associations of total and excessive GWG with mean BMI SDS and overweight were observed only in children of non- overweight mothers. For example, excessive GWG was associated with a mean increase of 0.08 (95% CI: 0.01, 0.15) units of BMI SDS (0.13 (0.02, 0.24) kg/m(2) of 'real' BMI) in children of normal-weight mothers. The effects of total and excessive GWG on BMI SDS increased for higher- BMI children of normal-weight mothers. Increased GWG is likely to be associated with overweight in offspring of non-overweight mothers.

In hoch entwickelten Wirtschaftssystemen unterliegen Banken einer besonderen Beaufsichtigung, da ein gut funktionierendes Finanzsystem die Grundlage einer soliden Wirtschaft darstellt. Insbesondere sind Banken verpflichtet, eine gesetzlich vorgegebene Eigenkapitaluntergrenze einzuhalten. Diese Grenze wurde in der Vergangenheit im Wesentlichen durch die Höhe der Bilanzaktiva bestimmt. Banken mussten für die aus diesen Positionen resultierenden Kredit- und Marktrisiken Eigenkapital vorhalten. Übrige Risiken wurden nur implizit abgedeckt. Durch die neue Baseler Eigenkapitalvereinbarung, die eine Empfehlung eines Ausschusses von Vertreten der Zentralbanken der großen Industrienationen darstellt und zurzeit in die jeweiligen nationalen Rechte umgesetzt wird, sollen nun unter anderem zusätzlich operationelle Risiken explizit mit Eigenkapital hinterlegt werden müssen. Zur Berechnung des notwendigen Eigenkapitals werden in der Vereinbarung drei verschiedene Ansätze aufgeführt, von denen zwei lediglich einfache und vermutlich risikounabhängige Berechnungsvorschriften darstellen; der dritte Ansatz jedoch - der Advanced Measurement Approach - kann bei entsprechender Ausgestaltung risikosensitiv sein, da er die Entwicklung und Verwendung selbst entwickelter Verfahren zur Bestimmung des Kapitals gestattet. Typischerweise werden bei solchen Verfahren Methoden aus der Versicherungswirtschaft verwendet, die Fragen zu Risiken von Prozessen, Personen, Technologie und externen Ereignissen bereits seit längerer Zeit zu beantworten versucht. Dazu werden die Ursachen der in der Vergangenheit aufgetretenen Verluste analysiert, um die aktuelle Gefahr zukünftiger Verluste zu ermitteln. Bei der Quantifizierung von Risiken in Banken müssen sehr hohe Quantile bestimmt werden, damit sichergestellt ist, dass das Unternehmen mit großer Wahrscheinlichkeit nicht zahlungsunfähig wird. Dies ist auch bei operationellen Risiken der Fall. Im Gegensatz zu Markt- oder Kreditrisiken stehen jedoch bei diesen nur relativ wenige Daten zur Verfügung. Dennoch wird in vielen zur Zeit verwendeten Modellen die Sensitivität der Ergebnisse aufgrund dieser sehr geringen Datenbasis nicht oder nicht ausreichend berücksichtigt. Die vorliegende Arbeit stellt ein Verfahren vor, um Konfidenzintervalle für geschätzte typische Risikogrößen wie z.B. einen Value-at-Risk oder den Expected Shortfall zu ermitteln. Die Anwendung wird dann anhand beispielhaft generierter Daten dargestellt, wobei die spezifischen Eigenheiten operationeller Risiken berücksichtigt werden. Dabei zeigt es sich, dass die bestimmten Konfidenzintervalle - abhängig von der für die Schätzungen verwendbaren Daten - mehrere Größenordnungen umfassen können. Bei der Interpretation der Daten und der daraus folgenden endgültigen Bestimmung von Mindestkapitalanforderungen für operationelle Risiken bei Banken müssen dann derartige Unschärfen berücksichtigt werden.