UNIVERSITÀ DEGLI STUDI DI PISA
Dipartimento di Economia e Management
SCUOLA SUPERIORE UNIVERSITARIA SANT’ANNA
Istituto di Economia
MSE - MASTER OF SCENCE IN ECONOMICS
AN ESSAY ON THE DISTRIBUTIONAL PROPERTIES OF REAL
ESTATE PRICE AND THEIR ECONOMIC CONSEQUENCES
TESI DI LAUREA MAGISTRALE
ABSTRACT
Using a dataset of real estate transactions based in New Work City and spanning from 2003 to 2016, the characteristic of the Big Apple real estate market has been studied as stemming from the empirical evidence on the statistical distribution fit. Indeed, every statistical representation entails some technical properties, that can be interpreted in an economic fashion. In particular, fat-tailness and weak evidence on the presence of a Central Limit Theorem dynamics underlying the empirical distribution has been detected from the results delivered by the Goodness of Fit analysis on the yearly and borough level datasets. It entails a high degree of market heterogeneity and the presence of an economic mechanism correlating the magnitude of the observed prices, stemming from three dimension: residential properties market, real estate market as a whole and its price per square feet. Then, a double-regime dynamics has been discovered at the borough level, where the speculative activity is stronger, in line with the most recent findings on U.S. income. Finally, a brief analysis of New York City residential market is delivered from the distributional standpoint of AEP, the best fitting distribution between the analysed ones. The main findings are consistent with spillover effect and spacial heterogeneity.
Aknowledgements
This thesis is the result of six years lived across Siena and Pisa, inside and outside of the university borders. It would has never been written without the growth of my forma mentis, which has been strongly influenced and shaped by the juicy disputes on politics and economics, that brought me along all over this period in some unique cultural environments. Firstly, I wish to thank my family for always supporting and believing in me and in my decisions, even when I changed three Bachelors in two years and when I chose to join a PhD, something that was completely unbelievable and unexpected when I got out of high school, given my intellectual skills at that time. A special thanks must be dedicated to my supervisor, Professor Andrea Roventini, for patiently and kindly helping me with the thesis and other non secundary issues. Then, I would like to thank for the insightful discussions and helps Professors Giovanni Dosi, Fabio Vanni, Mauro Napoletano, Alessio Moneta, Giorgio Fagiolo, Francesca Chiaromonte and Giulio Bottazzi and all my fellow economists, in particular Niccolò Ciuffreda, Oriol Gisbert, Matteo Coronese, Gianluca Pallante and my PhD colleagues, a group of great people who helped me in winning the fight with our schedule. Last, but not least, I am pleased to thank all my friends and comrades for those unrepeatable and crazy years in Tuscany, specially my historical groups from Lucca and Siena (you know who you are, you are too many to be listed), for their everlasting friendship and all the nights and the moments enjoyed together, Mario Dimonte, for chasing me until Pisa and for always being there, Luigi Schiavo, for teaching me how good are fried eggplants and zucchini, and the Spy Master people (Fanny comprehended), Nick and Uri, without whom Vettovaglie will never be the same again.
INDEX
1. Introduction – p. 1 2. Literature Review – p. 5
2.1 The empirical side of real estate dynamics – p. 6 2.2 The theoretical side of real estate dynamics – p. 14 2.3 A brief conclusion – p. 21
2.4 Figures – p. 22
3. The institutional and spacial context of New York City – p. 23 3.1 The legislative framework – p. 24
3.2 The boroughs – p. 27 3.3 A brief recap – p. 30 3.4 Figures – p. 31
4. The analysis of price distribution and New York City real estate market – p. 34 4.1 Data – p. 34
4.2 Methodology – p. 38 4.3 GoF results – p. 45
4.4 The economic consequences of the fit – p. 53 4.4.1 The price distribution – p. 53
4.4.2 New York City residential market – p. 62 4.5 Tables and figures – p. 70
5. Conclusion - p. 111 7. References – p. 113
1. Introduction
Why real estate prices? - Real estate markets are one of the most characteristic and leading branch of any developed economy. The object of a real estate purchase can be either newbuilt or “second-hand”. Its production, that takes months to be finished and years to be planned, is a very expensive and labour intensive process, acting as one of the main drivers of a large number of other productive sectors (production of material and intermediate goods for construction above all). A real estate property can be bought for speculation or for physical use, however it is usually needed a mortgage, and the market structure is extremely heterogeneous, both in the real estate property purpose and in its specificities, built in order to meet the preferences of a complex and differentiated public. Finally, every household needs at least one dwelling in possession, as a property or by renting it, and almost every firm a place where its productive activity can be pursued. Those characteristics give a clear hint about how the real estate market must be highly considered by economists. Indeed, a huge number of issues falls from its activity. Inequality, segregation and gentrification process, economic growth and production, credit market and business cycle, the dynamics of price determination are only some of the possible themes linked by real estate markets. In this thesis, the latter will be addressed from the perspective of the real estate price statistical distribution, using a dataset of real estate price transactions based on New York City boroughs, in line with the needs falling from the critical issues for macroeconomics raised by Stigliz (2011). In fact, the existing stream of research on real estate price distribution clearly proved its fat-tailness (Ohnishi et al. 2011, Blackwell 2016) and skewness (Coad 2009) as departure from the Log-Gaussian benchmark and as (partial) fit to the Power Law. However, it was not possible to provide a tidy understanding of the underlying statistical distribution. This gap makes it difficult to properly analyse the economic consequences stemming from the properties of a specific data-generating process. Indeed, studying real estate price statistical distribution could entail useful empirical evidence for the sake of modeling and of the description of the mechanism underlying the market dynamics.
Given the complexity of the issue to be disantengled, a taxonomy of the possible research heuristics could help in understanding the usefulness of the understanding of the underlying
distribution. However, it has to be considered that the frontier between the different arguments is fuzzy and do not allow to analyze any of the following points in isolation with respect to the other. Firstly, what are the determinats of the distributional dynamics? The wide support could be the consequence of high real estate market heterogeneity, whose level is determined by the degree of fat-tailness of their distributions and caused in turn by income and wealth inequality. Hence, possible similarities between wealth, income and price distribution could emerge from the statistical analysis, entailing a certain degree of correlation among pricing behaviour. Moreover, the degree of fat-tailness could depend on the state of the market, i.e. speculative dynamics or fire sales, and on expectations by the economic agents. As a consequence, some statistical similarities could be witnessed between the price and the credit distribution. Secondly, diverse institutional assets and geographical constraints entail differences in the distributions between and within cities or countries. The understanding of their influence could hint interesting clue on how the price spillover works at the distribution level. Thirdly, its evolution in time and space in terms of frequency around the mode or far from it could be interpreted as the response (or the cause) to business cycle movement or to policy covariates. Then, the principle underlying this thesis is straightforward, since the understanding of the evolution of price distribution according to the micro and macro economic dynamics could frame important answers not only in terms of policy and stability concerns, but also from the modeling point of view. For example, the empirical evidence on the functional form and the statistical properties of the real estate price distribution could be used to validate or structure models.
The core analysis of the thesis – The spacial and temporal study of price distribution will be carried on by fitting the Normal, the Power Law and the Asymmetric Exponential Power (AEP) distribution family (Bottazzi and Secchi 2011), a generalization of the Subbotin distribution (1923), as done for electricity price returns in Bottazzi et al. (2005), to the price microdata and its logarithm, respectively. The estimation of the latter has been performed through SUBBOTOOLS1 software(Bottazzi 2004). Thanks to AEP flexible parametrization,
consisting of 5 variables (positioning parameter, right and left scale around the the latter and shape of the tails), it is possible to account for asymmetry and fat-tailness. In fact, since the
Normal and the Laplace distributions constitute a particular case of the AEP distribution, it will be tested through the visual and Goodness of Fit (GoF) analysis if and to which extent the empirical distribution of NYC price logarithm, in absolute and per square feet terms, has a Gaussian or, more generally, an AEP shape and whether it displays asymmetry and fat-tailness. The same exercise will be carried on to ascertain if the whole distribution and/or the right tail is well approximated by a Power Law. The importance of the latter consideration falls from the work of Nolan (2001), who proved how the Levy-Stable distribution, when it cannot be approximated by a Normal, is characterised by such a tail behaviour. If both the Normal and the Levy-Stable are ruled out from the possible statistical distribution describing the price and its growth, then the dynamics of real estate price cannot be assumed to work under, respectively, the Central Limit Theorem (CTL) or its Generalized version (GCTL), entailing interesting consequences from an economic perspective: the price distribution cannot be considered the result of the aggregation of i.i.d. shocks. This would imply the operativeness of a “higher” level mechanism, correlating all the price observations.
The methodological procedure will be as follows. Firstly, the Gaussian will be fitted to the log-price distribution. Secondly, once ascertained the weak fit of the Normal, the AEP will be assessed as possible data-generating process, again for the concern of the log-price. Thirdly, the Power Law will be fitted to the whole price distribution and to the log-price tail. Indeed, thanks to the contribution of Nolan (2001, theorem 1.12), we know that, when the stable parameter is smaller than 2, the Levy-Stable distribution has leptokurtic tails (only one on the right, when the skewness parameter is near to 1), that can be asymptotically approximated by a Power Law. As a consequence, since the real estate price empirical distribution, displaying one and fat tail, falls within the possible shapes of the Levy-Stable, some evidence of right fat-tail considered together a bad fit and/or a low dataset coverage by the Power Law on the price distribution implies a weak empirical evidence on the Levy-Stable, that can be refused as a data-generating process. The same exercise will be replicated for the tail of the log-price distribution, once visually ascertained the impossibility of a fully Power Law distributed dynamics.
The findings – The main finding are the weak empirical evidence in favour of the CLT/GCLT dynamics as the consequence of the strong refusal of the Gaussian benchmark and of the weak (or weaker) fit of the Levy-Stable distribution, based on the Power Law detection, the presence of a strong heterogeneity in every price dimension and the similarities between the residential price and the income distribution. Indeed, according to the visual and GoF analysis, the best-fitting distribution between Log-Normal, Power Law and Log-AEP is the latter: the first one is refused at every level, whereas the second one provide a good fit only for marginal part of the distribution and the latter performs relatively well. As already remarked, it entails a high heterogeneity in the market and the existence of a mechanism correlating the price observations, operative at the pricing behaviour level. Then, some economic clues on the distribution shape falls from the estimated AEP and Power Law parameters, since there exist only one and very fat tail, located on the right of the real estate distribution. The volume of the market depends on a huge amount of low-priced transactions and a few and very few high and very high low-priced ones. Hence, high heterogeneity and incompatibility with law of one price and representative agents emerges from the analysis. The upper fat tail dynamics has some consequences on the statistical dimension, given that some Power Law behaviour could not be ruled out in the boroughs where the speculative dynamics is stronger relatively to the rest of the distribution. This feature is shared with the income distribution, as it will be explained. Next, the price spillover from Manhattan, the so called Manhattan Effect, downward stickiness and pro-ciclicality in the right tail fatness emerge from the borough level market analysis thanks to the proxy provided by the AEP parameters.
The structure – The thesis consist of the following chapters and sections. In the first one, the literature on real estate economics is critically reviewed, detecting and illustrating the main streams of research. This work is carried on for both the empirical and the theoretical literature, that identify two different sections. The second chapter “The institutional and spacial context of New York City” provides a broad analysis of the real estate market of New York City from the legislative, institutional and spacial point of view. The third and core chapter of the thesis , “The analysis of price distribution and New York City real estate market”, is divided in the following 5 sections. Firstly, in the “Data” section the dataset and
its problems are accurately described. Secondly, in “Methodology” it is presented a resume of the fitted statistical distributions and their focal properties, on which the economic analysis will be based. Within the same section, the GoF statistical tests are described, enlightening their strength and weakness with respect to the type of analysis performed. Thirdly, the visual and GoF tests results are reported in “GoF results”. Because of the huge amount of tests and dataset, the methodology chosen to present the table is based on colours, in order to help the reader to grasp a first impression of what the results tell about the fit. Fourthly, in the section “The economic consequences of the fit” it is carried on an economic analysis based on the fitted distribution statistical properties. It is also presented a brief overview of the New York City residential market based on the AEP fits on the quantile and the monthly borough level distribution. Finally, a brief recap of the main results is gathered in the chapter “Concluding remarks”.
2. Literature review
Since the Great Recession, the literature on real estate economics have been blossoming from the empirical and the theoretical point of view, with an encountable number of contributions. The former are mainly focused on the U.S. because the initial dynamics of the crisis has been witnessed on its real estate market and because of the huge availability and quality of data. Indeed, the 2007 U.S. financial crash was brought along by a real estate dynamics à la Kindleberger2 (figure 1.1): between January 2000 and March 2006 the
Case-Shiller 20-city real estate price index rose by 76 percent in real terms and then declined by 36 percent between March 2006 and May 2009 (Glaeser 2013), skyrocketing from the late Nineties, stimulated by local dynamics (Case 2008). As a consequence, the prominence of housing market role on the scene of Great Recession cannot be neglected. Notwithstanding the attention of the literature toward the most recent phenomena intertwined with the real estate dimension, Glaeser’s (2013) contribution documents a systemic and wide presence of housing bubbles as a consequence of speculative activity in time and across different areas, urban and rural, in U.S. history. This is to say that, despite the recent explosion of real estate
2 - According to Kindleberger’s (1987) contribution, an asset bubble can be defined as “a sharp rise in price of an asset or a range
of assets in a continuous process, with the initial rise generating expectations of further rises and attracting new buyers - generally speculators, interested in profits from trading in the asset rather than its use or earning capacity”.
economics literature due to the Great Recession origination, housing bubbles have always been a systemic feature of U.S. economic history, but were never at the center of analysis unlike nowadays.
In this chapter the more distinct empirical and theoretical contributions will be reported, providing a broad picture of all the existing streams of research and of their main results. In particular, the empirical section will go through the analysis of the following topics, generally considered within Great Recession framework, describing its timeline and providing some interpretations: the real estate and the credit market, foreclosures and real estate wealth effect on the aggregate demand, the policy instruments, the typical time series of the housing cycle, expectations in real estate market, locality of the dynamics and agent’s heterogeneity. The theoretical section will deal with the two main approaches toward the description of housing market: Dynamic Stochastic General Equilibrium Models (DSGE) and Agent Based Models (ABM).
2.1 The empirical side of real estate dynamics
Real estate and credit market intertwining dynamics - In academic literature the level of liquidity in the economic system has been pointed as one of the main driver of real estate price increase before the Great Recession. Probably, the most debated topic around the interconnected dynamics of house and credit market is the role of cheap credit and subprime mortgages (Mian and Sufi 2014). A very recent and disrupting contribution come from Bhutta (2015), who documents the inflows and outflows of debt before and after the Great Recession. Using a panel of microdata on individual liabilities, he analysed the role of different investors in shaping the amount of private debt. On one hand, the increasing inflows of debt was led by real estate investors, whereas the role of first home-buyers has been modest in comparison. On the other hand, the increase in outflows in the crisis afterwards has been mainly caused by lack of new credit, not by mortgage default. This evidence sheds light on the principal role of credit crunch with respect to bankruptcy for the concerns of the post-crisis effect on aggregate demand. As a consequence, speculative activity would seem a major driver of house appreciation, as confirmed by the findings of
Foote et al. (2016) and Haughwhout et al. (2011), in line with Bhutta evidence. However, other authors underlines an important role of subprime lending in mortgage default in the crisis afterwards. The lowest half of credit score distribution experimented the highest increase in debt, living in high house appreciation counties. The share of defaulted debt recorded for the lowest credit score borrowers was 73% in 2007 and 68% in 2008 (Mian and Sufi 2015).
If the price raise was fuelled by positive, often over-reacting expectations on the future house value appreciation (Case and Shiller 2013, Piazzesi and Schneider 2009, Soo 2015), whose abrupt and unexpected turnaround was the local trigger to the systemic crisis, mortgage and real estate price dynamics were strictly linked, a tendency already highlighted by Case et al. (1995): the largest raise in household debt from 2000 to 2007 has been witnessed in high house price growth U.S. counties (Mian and Sufi 2015). Notwithstanding the role of real estate price, also income and wealth distribution conditioned mortgage and default dynamics. Areas with lower employment, income and credit scores had higher proportion of subprime credit (Mayer and Pence 2008). Indeed, in 2007 the increase in mortgage defaults was higher in counties displaying a larger share of subprime borrowers (Mian and Sufi 2009) and the main driver of debt decline in the post-crisis period was the fall in credit inflow, caused by house price fall and rising unemployment. Their increase was the consequence of liquidity shocks to household balance sheet (Mian and Sufi 2012) and foreclosures.
Hence, the credit dynamics influenced the trend of U.S. economy in the following time pattern. Firstly, before the crisis there has been a leading role of investors in driving house price dynamics. Secondly, the systemic risk of the system raised because of subprime mortgaging, principally by first-home buyers, that ended in the post 2007 default and foreclosure explosion. Indeed, Demyanyk and Van Hemert (2011) find evidence on the unsustainability of growth in the subprime lending market, that caused the collapse of the market itself. So, investors and high credit score borrowers could keep up with the economic and financial downturn in the Great Recession afterward, whereas low score ones started defaulting. The evidence at a higher level of aggregation than microdata confirms
this finding (Mian and Sufi 2009). Indeed, if the absolute value of mortgage originations was higher for the investors, the lowest part of credit score distribution experimented a more consistent growth of debt stock. Once the housing bubble burst, such a dynamics produced the disruption of aggregate demand via foreclosures, rising unemployment and credit crunch in two respects, home equity and mortgage fall. Hence, the main source of this policy disaster was the collapse of house prices. If this is true, that part of U.S. growth based on real estate properties was consistent with a sort of Ponzi-scheme, induced by cheap credit and increasing prices. Looking at data, this effect on growth has been negative. U.S. GDP started decelerating on the first quarter of 2006 with the housing bubble burst and at the beginning of 2008 U.S. officially entered the recession, that ended in the third quarter of 2009 (figure 2.1). After the crisis, the GDP growth seems to follow another trend, confirming that a huge part of its pre-crisis magnitude was caused by the housing and credit bubble. Indeed, the house price downturn emerges as a variable deeply intertwined with the pattern of recovery and with the depth of the economic bust through the channels touched by the foreclosure process, the topic of the following paragraph.
Foreclosures - Considered its influence on the financial sector, housing price and aggregate demand, the role of foreclosures in the Great Recession has been the focus of numerous studies. Ferreira and Gyourko (2015) point at foreclosures crisis as a prime, more than as a subprime borrower’s problem. LaCour-Little et al. (2011) discover a positive linkage between subprime piggybacks origination (a second mortgage made using as collateral a real estate property that has already been used to such a purpose) and foreclosures. Gerardi et al. (2015) assess a significant spillover effect from foreclosed properties, implying a double level of (intertwined) effect to price: the latter, local, and the credit crunch biting through the disruption of banks liquidity. Calomiris et al. (2008) find evidence of the bidirectionality of the causal relationship between price and foreclosures and, for the concerns of the real counterpart of its effect, that they negatively influence labour market dynamics, whereas Mian et al. (2011) observe a negative relation also with consumer demand and residential investment. Hence, in the light of the empirical evidence, the spacial propagation of the fall in price driven by foreclosures depends on two higher level variables: the institutional factors (Mian et al. 2011) and the level of income of the county, that, as it
decreases, determines a higher probability of defaulting subprimes and more potential foreclosures. In turn, foreclosures had a leading role in influencing the dynamics of recovery and the one of the economic bust through the house price dynamics. There are two possible explanation for its role. The first is the response of the construction sector to lower value of its productive output. The second and main effect is the lower liquidity amount and turnover in the system as the volume of the market collapses, that entailed less returns amount released through real estate transactions. In this sense, one of the consequences witnessed in empirical studies is the fall in consumption, partially caused by the dampening of home equity.
Home equity and wealth effect - On one hand, the level of liquidity and growth was influenced through residential investment. On the other hand, house price change determined real outlays consumption via home equity. Indeed, there was a wealth effect stemming from real estate properties appreciation, whose existence is endorsed by the empirical evidence and supported by large and positive estimates (Case 2005, Carroll 2010, Calomiris 2012). The economic argument underlying real estate wealth effect is as follows: if the value of real estate properties is increasing, agents will be likely to consume more income, given that they perceive the appreciation as a gain. However, the contribution by Bhutta and Keys (2014) and Mian and Sufi (2010) highlighted how the raise in house value entails a strong rise in aggregate demand due to the increasing use of home equity. For this reason, the estimated real estate wealth effect is likely to be positively biased: change in real outlays expenditure could partially depend on change in home equity driven debt, that is positively correlated with increase in house wealth. So, the change in consumption did not happen for free, but entailed an increase in systemic risk: Bhutta and Keys (2014) discloses how the use of home equity raises the likelyhood of default. However, the general message from those estimate implies a general tendency of real estate price dynamics to influence the aggregate demand via the magnitude of real expenditures. As a confirm, Mian et al. (2013) show how the wealth shocks falling from house bubble pop drove down consumption via home equity, in particular in highly leveraged areas. Hence, the crisis showed particular and local dynamics in home equity, as well as in subprime mortgages and foreclosures, making it difficult to track down the micro and meso mechanism of the Great Recession without
taking in account the characteristics of the real estate market at higher level of disaggregation than the macro one.
The local dynamics of real estate markets - The interconnection between price and real dynamics through foreclosures and home equity and the deceleration of U.S. GDP growth in the same moment of the peak of the housing market suggest a possible comovement of the housing cycle with the aggregate business cycle. The intuition strongly depends on wheter there is an economic relation from the local to the aggregate level, that is if the variables driving the business cycle are determined by the local trend. In fact, it must be considered that the local dynamics of housing cycle acts as a major qualifying feature for real estate markets. On the empirical side, the evidence on the spacial dynamics of the recent housing crisis, as assessed by a considerable number of authors, has not been straightforward. Within the U.S., cities and counties experienced very different price dynamics, accounting for both boom and bust, for only one or even none of them, and starting in different moment in time (Abel and Deitz 2010, Ferreira and Gyourko 2012). Heterogeneity is found also with regards to the bubble contagion dynamics at Metropolitan Statistical Area (MSA) level (Cotter et al. 2011), suggesting different level of market integration, and in timing, since it characterises more frequently booms, than busts (DeFusco et al. 2013). On the contrary, Zhu et al. (2011) found evidence of price volatility spillover only during the bust. Those studies charachterise the Great Recession as an upside-down funnel crisis: locally varying and idiosyncratic liquidity shocks to the banking system, stemming from a sudden, local and endogenous change in house price expectations entailed a credit crunch on a national scale. The flip from a local, to a global level of financial disruption was accomodated by the high level of financial integration, in particular concerning the securities sector, and by the low quality of the underlying subprime mortgages. In this sense, the importance of interconnection and integration degree in financial markets should be addressed by policymakers as a primary feature for systemic risk evaluation. However, the critical issues on policy management, concerning both the goals and the instruments to meet them, does not stop to this point. Indeed, the main driver of the mechanism appears to be the change in real estate price.
Policy measures - The effectiveness of the use of Loan-to-Value (LTV) and Debt-to-Income (DTI) as a dampening tool for house price has been proved by numerous contributions (Oh 2013, Igan and Kang 2011, Kuttner and Shim 2013, Jàcome and Mitra 2015). Indeed, the level of residential debt is the synonymous of financial markets expectations on the real estate market, represented by LTV and DTI level. The former started increasing in 2003 and reached its peak in 2007 (figure 2.2). The Combined Loan-to-Value (CLTV) ratio, the LTV computed on all the loans that has been securitized on a specific real estate property, followed the same dynamics on an enhanced level (Levitin and Wacther 2015, Duca et al. 2016), that generated a strong decrease of home equity. Moreover, heterogeneity in LTV ratio has been found on a local level (Lamont and Stein 1999). Counter-cyclically intervening on LTV and DTI, the policymaker is allowed to tackle what the historical lesson of Great Recession points to be riskiest credit behaviour that a lender can assume on the market, that is basing the creditworthiness decision about the borrower on the expectation of increasing collateral prices. In fact, Stockhammer and Wildauer (2017), studying the dynamics of 11 OECD countries, find that real estate price is the main driver of private debt increase between 1995 and 2007 and documents the positive role of interest rate in determining house appreciation. However, looking at the path of FED Effective Federal Funds rate, it is at least debatable their role in possibly stop a housing bubble, once that it started. Between June 2004 and July 2006, it was raised from 1% to 5.25%, but the housing boom did not feel it. A possible explanation could reside in Hilfering’s (1910) interpretation of interest rates. The interest rate level determines the amount of potential or realised profits that will be extracted from a planned or current investment for the benefit of banks liquidity, that is in this particular case the difference between the price paid for the house or its construction cost and its market value, determining the level of solvency in the system and shaping the state of profit expectations. This stream of research has been lately investigated by Brancaccio and Fontana (2016, 2013) and Brancaccio et al. (2015). Under this view, if the house price is exponentially increasing as happened in U.S. early 2000s, the level of interest rate theoretically necessary to overturn the housing dynamics would be too high to be beared by the economy. This is a possible reason why alternative instruments of policy, as specific macroprudential countercyclical constraint on individual mortgages, would be needed in the gun battery of policymakers.
Real estate bubble and its time series - The conjecture of the inability of interest rate to properly tackle a bubble is consistent with the convex and increasing shape of the price growth trend in housing boom, described by Kindleberger (1978) and coherent within Hilferding’s solvency role of Central Banks activity. It starts with a tiny increase. Then the magnitude of the price growth raises, delivering a convex shaped pattern of price. In the end, the time series reaches its maximum and can dramatically crash or slowly deflate. His descriptive picture is confirmed by Titman et al. (2014) and Cutler et al. (1991): the time series of real estate price shows positive serial correlation at the one year intervals and reversals of price changes over longer intervals. Glaeser et al. (2014), Wheaton and Nechayev (2004) found excess variability with respect to fundamentals. Capozza et al. (2004) notice the presence of heterogenous magnitude of serial correlation and of mean regression patterns in space and time, concluding that they are spacially and temporally determined by the interaction with other factors. In particular, the former increases with real income, population growth and real construction costs, while the latter is bigger in large metropolitan areas, growing faster and at lower costs. Genesove and Mayer (2001) give a behavioral explanation to the positive, lagged correlation of housing volume and price (Miller and Sklarz 1986), analyzing the selling decisions of real estate investors: they act according to the theoretical findings of Prospect Theory (Kahneman and Tversky 1976), entailing downward price stickiness. Indeed, economic agents appear to behave according to bounded rationality and extrapolative rule of thumb decision, in consistent deviation from rational behaviour.
Expectations - Soo (2015) finds out that housing media sentiment, used as proxy of beliefs, not only has a good predictive power for real estate price, but even performs better than historically predictive factors and past returns. Piazzesi and Schneider (2009) assess the heterogeneity and pro-cyclical endogeneity in beliefs, making a cluster analysis of them: a group of “positive believers” is always present and grows in size with the housing boom. The contribution by Clapp et al. (1995) poses the accent on the overemphasization of the latest real estate dynamics. The role of short term rate of change of price frames it as an important decision factor in the eyes of agents. On the contrary, Case and Shiller (1988,
2013) found out how short run expectations underreact to market dynamics, whereas the long run path seems to be preminent in driving agent’s decisions. The most striking evidence has been delivered by Cheng et al. (2012), who assess the incompatibility of forward-looking and rational beahviour with finance specialized, high skilled workers, who were not able to predict the housing bubble crash, neither they were seemengly conscious of its possibility. With this respect, the empirical evidence on real estate price expectations is clear: agents do not behave according to forward looking expectations. They do not base their purchasing decision on the evidence of long run mean revertibility of real estate prices, even if they are specialised investors. Rather, they act on the beliefs that prices will undefinitely increase. Hence, rule of thumb decision based on extrapolative, naive expectations is the better describing behavioural pattern. Instead, it is not clear which has the leading role between short and long run expectations, nor, empirically, how expectations are driven and influenced by the individual level of wealth and income. However, it is possible to define a taxonomy of the various agent’s types, falling from their actions, that is likely to determine the emergence of a certain state of expectations in the real estate market. As usual, the frontier dividing them is not sharp, because an agent can occupy more than one position.
Agent’s heterogeneity - The housing market is grounded on a complex ensemble of interactions, determined by the set of decisions falling from the individual wealth and income of the agent: renters, who can become first-homebuyers, owner-occupying agents opposed to investors and rentiers. The pattern entailed by their actions before and after the Great Recession is considerably different (Bhutta 2015, Haughwout 2011), as the object of their transaction (Glaeser and Gyourko 2007, Chiodo et al. 2010). Their credit possibilities and their beliefs shapes the housing market demand and are the leading candidate to induce the emergence of bubble dynamics, as the very recent evidence of Adelino et al. (2017) and the seminal work of Case and Shiller and Mian and Sufi confirms. Levitt and Syverson (2008), studying the approach to real estate market of specialised and not specialised agents, highlight how the asymmetric information between agents can deliver market distortion in terms of sale price and time on the market. It is the consequence of the cumulative characterization of knowledge and of the absence of perfect information in real estate
market, from which non-rational expectations emerge as the leading beahvioural pattern: if the information about the future was perfect and symmetric, the specialised seller would not earn more from their sale.
2.2 The theoretical side of real estate dynamics
The increasing empirical evidence based of microdata, the use of innovative instruments of policy and the break down of widely accepted and modelised distributions underlying economic phenomena caused by the crisis has raised new issues for standard economic modeling. Indeed, up to the Great Recession the research efforts on DSGE were aimed at explaining small variations in fundamentals and the response to small shocks. (Stiglitz 2011). The adoption of the exogenous, often Gaussian, shock structure does not make DSGE the proper way of modeling in order to address the emergence of endogenous cycles with “big” change in the growth dimension, the amplification and persistence of downturns and the patterns of recovery from them. Indeed, even in presence of a Laplacian structure, the standard 3-equations DSGE has been proved to fail in the replication of fat-tailed output growth rate (Ascari et al. 2012), meaning that the mechanism of the model dampens the strength of big shocks. Another critique raised by Stiglitz (2011) is the absence of a micro-structure consistent with the needs of the bottom-up depiction of an economy. This means distribution-based heterogeneity, but also boundedly rational behaviour, whose presence is very useful for the representation of structural and endogenous transformation in modeling. If the aim of the model is the representation of the emergence of a housing bubble, the caveat is that the taxonomy of agents and their behaviour must react to change. So, an exogenous and static heterogeneity and the introduction of behavioural patterns changing as the consequence of a new binding constraints or of a belief shock can depict only the reaction to change, but cannot explain how the economy got there. Hence, I will try to argue how, for the concerns of the description of the real estate market, Agent-Based-Models are and need to be considered a complementary instrument with respect to DSGE, that still remains a very useful tool to study the response to shock through the Impulse Response Function analysis. That is to say that the efforts are not aimed at ruling out DSGE, but at
highlighting its limits in the framework of endogenous cycle analysis, where the ABM structure seems to perform better at the moment. The point will go through the analysis of the critical issues not allowing the real estate augmented version of DSGE to grasp the endogeneity of the cycle, namely agent’s heterogeneity, banking sector micro-structure, expectations and endogeneity. Then, it will be provided a brief comparison between ABM and DSGE, a review of the (few) ABM on the topic and the reasons why real estate price distribution matters.
DSGE and heterogeneity - For the concerns of real estate markets, very recent develompments have been made in heterogeneity, entailing very good theoretical results in terms of real estate market description. Huo et al. (2016) overturned the heterogeneity based on the patient-impatient duality (e.g. Iacoviello 2005, Justiniano et al. 2015), in an exogenous skill distribution based DSGE. As a consequence, the magnitude and persistence of downturns, stemming from real estate exogenous shocks, are strongly amplified. However, the heterogeneity is based on an exogenous Markov process driving the workers’ skills and affects the dynamic of the model only through the probability of being fired, introducing demand externalities and drops in earnings in case of crisis. Favilukis et al. (2017) DSGE model grounds the micro-structure on a bequest distribution, a data-driven heterogeneity with heritages between overlapping generations, described as to be fat-tailed. However, the “heritaged” fraction does not change with distribution of income and wealth from one generation to another, so the model results are characterised by a static mechanism in the determination of housing demand. Favilukis et al. shares a similar exogenous heterogeneity in agent’s income with Kaplan et al. (2017), where income depends upon shocks to productivity plus an age specific component and exogenous stochastic part, and with Iacoviello and Pavan (2013), Guerrieri and Lorenzoni (2011), Greenwald (2016). Concluding, the income heterogeneity structure is based on an exogenous fully Normal or Log-Normal distribution (against empirical evidence, Dragulescu and Yakovenko 2001) or on a Markov chain, subject to shock in order to simulate the fall in income, that is likely to occur in a real estate driven crisis and to generate worse borrowing conditions through various borrowing constraints.
DSGE, bank micro-structure and locality - Another common issue to standard economics family is the absence of explicit micro-modeling of the banking sector, that is assumed to be homogenous and is modelised as a borrowing constraint. However, the evidence tell us that there is heterogeneity in bank size (Ennis 2001, Berger 1995) and, thus, that a representative bank does not exist in reality. Indeed, they enter DSGE model dynamics only as regulators of liquidity supply, depriving them of their role of risk taking entities. Hence, it is not possible to account for endogenous shocks generation, amplification and propagation falling from idiosyncratic bank liquidity problems, in turn caused by real estate market downturn. On the borrowers side, credit is usually subject to exogenous shocks to imitate a credit crunch. Accordingly, non-standard monetary policy does not find a fertile environment to be developed. The study on counter-cyclical LTV by Lambertini et al. (2013) constitutes a notable exception in terms of policy, but the model needs to smooth the results around a stable equilibrium through a borrowing constraint, as explained in Farmer et al. (2016). Piazzesi and Schneider (2016) suggest possible benchmark for real estate modeling. The financial shocks need to work exogenously through shock to the LTV ratio, as in Kaplan et al. (2017), eventually dampening the effect of house market crash because the borrowing constraint is endogenously operative only thanks to price, computed as the sum of present value of future returns. Guerrieri and Lorenzoni (2011) introduces a shock to credit. The common setting of standard economics models does not embody a real estate spatial structure too, making it difficult to explain the local features of housing markets or large and persistent crisis emerging from local shocks, whose the Great Recession is the most recent case. Resuming, the critique by Glaeser (2007) is at work: firstly, the credit is exogenously determined through shocks to labour productivity/income and credit, hence DSGE cannot explain the origination of a credit crunch; secondly, locality is never modelised, making it impossible to witness any spacial dynamics.
DSGE and expectations - Finally, the absence of bounded rationality makes it difficult to explain the dynamics of real estate market, that is strongly characterised by systemic deviations from fundamentals and by cyclical bubble-type dynamics. Here, the assumptions consists in making the bubble “rational” and to let the connection between growth and price work through real estate wealth effect and home equity at the same time, even if, as
discussed above, it is likely their overlap from a statistical point of view. Guerrieri and Uhlig (2016) modelise a “greatest fool”, who is willing to bet that house price will rise again in the future and to whom the rational agent could sell the house bought at today price, gaining a positive profit. However, he does not exist in the dynamic of the model, fooling the other agents. In Burnside et al. (2011), the model structure is augmented with heterogenoeus expectations and exogenous belief spillover among agents. A negative exogenous shock to expectations completes the pictures and renders possible the housing cycle, otherwise not emerging endogenously. However, the last two theoretical enhancements do not explain the endogeneity of expectations and of real estate cycle and, as explained above, cannot be considered a transition to naive expectations. Another example is the brand new overlapping generation model by Kaplan et al. (2017). The authors address the effect of credit constraint, labour productivity and beliefs on real estate price. Introducing a positive “big”, i.e. with low ex ante probability, shock on each of them and subsequently overturning them, a boom/bust cycle emerges. In particular, beliefs are assumed to be equivalent to preference toward housing. The model main results are two. Firstly, the importance of expectations for the crisis dynamics emerges as the main driver of price change. However, their formation is exogenous, so within the model there is not a policy able to avoid house bubbles within the model, considered that, secondly, house price results to be independent on credit conditions. A similar critique has already been formulated by Lindè et al. (2016) about financial accelerator DSGE models, but it can be expanded to the real estate branch of standard macroeconomics. Indeed, Favilukis et al. (2016), Landvoigt et al (2012) and Justiniano et al. (2015) find the opposite result in terms of price-credit relationship. The very different results in policy stemming from DSGE models are the symptoms of the lack of a standard methodological approach: there is not a precise benchmark structure, besides the equilibria and the “rational” bubble requirements.
DSGE and endogeneity - A general problem falling from the absence of endogenous heterogeneity in income, expectations and credit conditions is the impossibility to account for real estate distribution and for inequality concerns. Indeed, if the heterogeneity is badly modelised, the price distribution and the real estate returns are not likely to generate a clear or realistic shape, nor the income and wealth distribution will determine the credit
possibilities through LTV and DTI. As a consequence, it is never analysed in standard macroeconomic models how real estate price distribution stems from an endogenous context, falling from the intertwining dynamics of heterogenous beliefs and credit constraint. The same goes for property income/returns and wealth distribution, that are likely to influence the credit constraints and to be influenced by the model mechanism, and for mortgage distribution, that has a bidirectional causal relationship with prices: it is caused through LTV and real estate value, and it also causes it, pleasing agents’ liquidity problems and fulfilling price expectations. In such a context, abrupt changes in distributions as a consequence of boom and busts cannot be studied or used for output validation purpose, as well as the role of different categories of agents in determining the inflows and outflows of debt will be unclear in the model dynamics, because they will not yield a good fit with empirically validated functional form. Considered the primary role that heterogeneity and endogeneity in income and expectations had in the Great Recession dynamics, its absence or exogenous configuration is a non trivial problem that standard economics must deal with, in order to properly represent the above discussed mechanism.
DSGE vis à vis ABM - The joint absence of bounded rationality, bankruptcy, spacial structure, non-Gaussian dynamics, agent’s, bank and house price endogenous heterogeneity undermines real estate augmented DSGE ability to properly explain how out-of-equilibrium dynamics can be triggered by the housing market activity, that is in turn endogenous on wealth, income and liquidity. As a consequence, all the intertwining dynamics and trade-off intrinsec to a complex and evolving system cannot be grasped by the existing theoretical structure of DSGE models (Fagiolo and Roventini 2016). In this sense, there are two order of reasons why the complementary perspective of ABM is needed to handle endogenous credit and housing cycles. Firstly, the heterogeneity of expectations, houses, banks and agents’ income and wealth entails inequality concerns, heavily affecting housing and credit cycles. By now, those cannot be, analysed by standard macroeconomics from the distributional standpoint. Secondly, the intertwining dynamics of policy, real estate and imperfect credit market are able to replicate non-linear patterns, originating from endogenously emerging non-Gaussian, fat-tailed distributions. Such a process can be replicated only in an endogenous cycle context with bankruptcy possibility for both
heterogenous banks and households. However, the change in methodology from a mathematical and axiomatic to a computational based approach brings along a complete different view of the economic system: the existence of “natural” level of fundamentals withstanding a stable equilibrium, result of maximizing behaviour, gives way to a complex and continously evolving system, characterised by endogenous cycles, generating from coordination failures and by endogenous states of expectations. The latters are represented through bounded rationality and overlap over time, shaping the macro dynamics. It is not a case that the widely accepted methodological trick to linearise DSGE models around a stable equilibrium in order to fulfill equilibrium requirements, is not considered necessary in ABM out-of-equilibrium dynamics.
Real estate augmented ABM literature - The literature on ABM recently focused much attention on the relationship between credit and housing market. Raberto et al. (2013) build a real estate augmented model on the platform EURACE, in order to study the relation linking the access to credit with the economic stability. In line with the above mentioned evidence on LTV and DTI ratio and with Mian and Sufi seminal work, they found an important driver of downturns in loose access to credit. Farmer et al. (2016) use London institutional context and data to create a model with the aim of studying the effect of macroprudential policy on some leading real estate market indicators. It entails the dampening of the price dynamics. The main characteristic of the model is a highly complex micro-structure, that embodies all the possible different types of agents operative in the housing market. Geneakoplos et al. (2014) simulates the real estate market of Washington D.C. area with very good results in terms of replication of the rich amount of datasets available to the authors. The model focuses its attention mainly on the financial and the house market mechanism, confirming Geneakoplos (2010) intuition about the leverage cycle: a variation of asset prices entails to a change in the leverage conditions, even without movements the interest rate. As a consequence, the conditions through which borrowers can subscribe a mortgage must be monitored by policy-makers to avoid bubble insurgence. Instead, Gangel et al. (2011) focus on the spacial environment to study the effect of foreclosure on real estate price dynamics in a stylized economic structure. The authors find out an interesting result, in line with the above mentioned empirical evidence on
foreclosure: the more foreclosured properties stay on the market, the higher is the probability of market failure.
ABM and real estate price distribution - If the house price distributional concern is absent in the DSGE stream of research, in the ABM the situation is very different. For the sake of the mechanism based on heterogeneity, prices are partially modelised as Log-Normal in Farmer et al. (2016), where an idiosyncratic shock enters additively on the price logarithm decomposition, and in Geneakoplos et al. (2014), where a Log-Normal function multiplies the ratio of variables on which the price bases its endogeneity. Moreover, if the log-price distribution is fat-tailed, then the right tail of the price distribution decades very slowly and the frequency is less concentrated around the mean with respect to the case of a Normal-tailed log-price distribution. Since, the logarithm dampens the right tail distribution, if the log-distribution is fat-tailed, the prices shape will be extremely leptokurtic. Again, the economic consequences are not trivial. Indeed, the intertwining dynamics of housing and credit market have been object of a growing interest by economic literature in the last decade and it has been suggested how financial and real crisis are generated by real asset bubbles fueled by mortgage dynamics (Brunnermeier et al. 2012). As a consequence, even a partial assumption of Log-Normal structure of real estate price distribution is likely to dampen the model dynamics. The ex-ante low probability of individual mortgage generated within the model could not be enough, firstly, to resemble the statistical properties of inflows and outflows dynamics of a housing cycle and, secondly, to drive up the individual prices through expectations based on extrapolative measure of prices, that enters the price function of both the models. Hence, the growth of the systemic risk could result lower than assuming a more fat-tailed distribution, as extreme price and mortgage would appear less frequently. However, since, the price structure is not fully Log-Normal, the models should be run with a more fat-tailed structure in order to assess this supposition and to validate their output price.
The Great Recession dynamics was the result of the intertwining behaviour of credit and real estate markets. The pre-crisis inflow by investors and subprime borrowers feeded the housing bubble, that entailed some economic growth before the bust. However, the downturn proved the contradiction of a mechanism based on the hope of indefinite private debt increase to crowd out the effect of the polarization of income and wealth. In this sense, U.S. economy showed the characteristics of a complex system. Firstly, the post-crisis dynamics of foreclosure, credit crunch and home equity generated a fall in price, investment and consumption, that played on the other way round with the credit dynamics. Secondly, the local dynamics was determined by the heterogenous material conditions and expectations of the agents. In this framework, the inability of policymaker to effectively oppose the price boom was due to the use of standard measures: the interest rate increase was not enough to stop the local persistence of the bubbles. The need of new weapons in the hand of Central Banks is straighforward for the management of real estate bubbles, as demonstrated by other experiences than U.S.
The complex characterisation of the relationship between house and credit markets entails some theoretical consequences. Firstly, General Equilibrium models cannot deal with the emergence of bubbles from this complex economic mechanism. The degree of endogeneity of the real estate price strongly depends upon the assumptions concerning the micro-structure and the shocks. Even in the most recent models, the necessity of rational expectations rules out the endogeneity of the housing cycle, that mainly shows its effect on the growth dynamics through the coexistence of wealth effect and home equity, proved to be unconsistent by the empirical evidence. Secondly, a bottom-up and data-driven approach to validation, based on distributional facts, is not taken into account, since the same absence of distribution based (endogenous) heterogeneity renders impossible to replicate the empirical evidence on data-generating processes, as the material conditions determines the credit and real estate price dynamics. In this sense, Agent Based Modeling seems the only possible alternative to properly represent the complex dynamics underlying the housing cycles in all its possible facets, given that it strongly borrows from the concepts of bounded rationality as a proxy of extrapolative expecations. Indeed, within an ABM framework, the cycles do not
need to be exogenously pushed through shocks and the disequilibrium dynamics makes it easy to carry the emergence of fat-tailed distributions across the model.
2.4 Figures
Figure 2.1 U.S. seasonally adjusted real GDP . The grey shaded area covers the NBER recession period. Source: FRED seasonally adjusted real GDP time series.
3. The institutional and spacial context of New York City
Chapter 4 will go toward the analysis of the real estate price distribution and of its best statistical fit. In order to do that, the New York City real estate price dataset3 has been
considered and divided in 70 different datasets, 5 different boroughs (Manhattan, Brooklyn, Queens, Staten Island and the Bronx) per 14 years (2003-2016). The provision of a comprehensive analysis, able to take account of the differences in the boroughs and pre/post-crisis distributional heterogeneity, is diriment for the complete and correct depiction and interpretation of the economic consequences springing from the statistical analysis of the distribution. This is the reason underlying the inclusion of this chapter in the thesis.
A general introduction - According to Case and Shiller New York City home price index (figure 3.1) New York City housing market went through a boom, started in 1996, and a bust, covering the years between 2006 and 2012. After 2012 the price recovered its pre-crisis level, however the growth trend has been far to be as steeper as the boom period. In comparison to the national index, New York City experienced a dynamics more bubble-shaped, sticking to Kindleberger definition, with a higher peak in 2006: with respect to the base year 1996, the start of the boom, the national housing cycle has been on a lower level of magnitude in terms of house appreciation growth. Within NYC, the spacial dynamics of the boom has been driven by the “Manhattan effect”: Manhattan has been the center of speculation with spillover effect on the prices of the closest neighboorhoods belonging to Queens, Brooklyn and the Bronx (“Trend in NYC housing price appreciation” 2008), whereas Staten Island was not touched by the golden fingers of Manhattan price spillover.
The timing of 1996-2006 house boom end is polarized, according to the Furman Center all property type price index (figure 3.2), computed on repeated sales of the same properties, and precedes the crisis at national and at Metropolitan Statistical Area level (figure 3.3), started before 2007. All the boroughs, with the exception of Manhattan, experimented a 5 years long negative trend from 2006 to 2011. On the contrary, Manhattan house depreciation
lasted only one year, 2008, in which the two cycles started diverging, converging again to a similar pattern in 2011. In that year the appreciation series of every borough started to draw a convex function, the typical asset bubble shape. However, the differences across borough house cycle go beyond the average residential appreciation, that represents the spacial position of the distribution of price growth, since, as it will be explained in the next chapter, the dynamics of the price distribution tails shows different movements and instensities. In order to provide a formal economic analysis of the factors possibly causing them, a comprehensive framework about the most important legislative and geographic features of NYC real estate market need to be disclosed together with the analysis of the borough level factors able to influence the real estate market dynamics. The former will focus on the Floor Area Ratio (FAR) and on the policy measures adopted to tackle the house emergence, the latter on the population characteristics and geographical constraints (figure 3.4 for the map of New York city).
3.1 The legislative framework
The Floor Area Ratio - The urban planning is grounded on a constraint, called FAR, restricting the possible floor area of new buildings on a specific zone. It is the ratio of the building total floor area to the size of the piece of land upon which it is built. To make an example, if a construction must stick to a 2 FAR, then the sum of all the individual floor area existing within the building must not exceed two times the area on which it insists. In this case, it is not possible for a new building on a 150 squared meters plant to exceed 300 squared meters of total floor area. Hence, the regime of FAR control determines the urban fabric of New York City, limiting the presence of skyscrapers, the real estate supply and the elasticity of house volume, the speculative potential of land exploitation for the construction sector and for investors. As a consequence, it defines the urban scope of the diverse boroughs, that is possible to infer looking at their decomposition in terms of income, populaton and state of the real estate market, kindly provided by the Furman Center of Real Estate and Urban Policy.
The regime of control of the real estate market is rooted in a much more complex issue, that is the need of demogaphic control, of a sustainable urban planning, that can take account of the necessity of infrastructures in order to keep up with a fast-changing environment, and of speculative activity. Indeed, the FAR zoning regulation was born in 1916, aiming at overcoming the lack of air and light caused by the buildings overcrowding, and it was modified in 1961 to allow for incentive zoning, that is the creation of public amenities/infrastructure by private in exchange of more floor, for open spaces and for the automobile era. In figure 3.54 it is possible to take a look at the various regimes regulating
New York City zoning in terms of average FAR. Looking at the map, two facts emerge. On the first place, on average Queens and Staten Island are not as “high” as Manhattan and the Bronx. Brooklyn is halfway between the two of them. On the second place, there has been a “Manhattan effect” also in a FAR spillover to its closest neighborhoods. It is the probable consequence of the role of Manhattan as the local business center, that made necessary to expand vertically the real estate supply even outside of it.
NYC housing emergency and policy measures - New York City has been in a housing emergence during the latest century. The vacancy rate has always been very low in every borough, and the ownership rate stayed steady across the years. Rents and house price kept on rising, as the rate of severly rent burdened people. From this point of view, the best performing borough is Manhattan, despite the highest prices, suggesting how the economic situation of NYC is unequal and heterogenous in terms of income. Regarding the rent time series, in spite of the fall in real estate price, income, local GDP and the raise of unemployment, its slowly increasing trend marginally the bust (figure 3.6). It could happen for two reasons.
Firstly, the emerging picture hints that the supply of house did not keep up with the demand, that was trained by the population increase from 8 million to 8.5 million of citizens. Indeed, the subscription of new building permits dramatically fell in 2009 (figure 3.7), never recovering the overall pre-crisis level with the exception of 2015, when it strongly outpaced even 2008 level. In fact, on the beginning of 2016 the 421-a property tax exemption GEA
4 - The picture has been kindly provided by J. Barr at the following website: https://blog.oup.com/2016/09/new-york-housing-crisis/
program expired, encouraging constructors to exploit the latest chance to implement their postponed building plans. Moreover, the change in housing supply, measured by the new certificates of occupancy5 (figure 3.8), diminished, but not as dramatically as the permits,
because of the slow entrance of the buildings already under construction. The second, straightforward reason is the nature of rental contracts. Being long term commitments, the rent amount is sticky by definition with respect to the cycle. Another variable influencing rent trend is New York City regime of rent control, that is likely to moderate the rent movement both upwardly and downwardly. However, the existence of house burdening is not limited to renters: high costs connected to mortgages, taxes, house service and maintenance affect also home-owners. The situation has been tackled over the years with the two mentioned measures: the 421-a tax exemption declined on the Geographic Exclusion Areas (GEA) and the rent control regime. They both intervene on the monthly return from the real estate property, dampening the regulated house price. However, constraining the supply of non-regulated houses, it could also push up their price, polarizing the market and worsening the situation.
The 421-a tax exemption, dating back to 1971 consists of a 10 years building tax exemption for contructors implementing multi-unit residential project on vacant land. The GEA, covering Manhattan and some close areas, plus the northern part of Staten Island (figure 3.9), establishes the physical limitations by which the 421-a can be obtained only sticking to a voluntary measure created to increase the number of affordable housing. If more than 25% of the units within the newbuilt building is devoted to affordable housing, the constructor gets the exemption, rebalancing the higher possibilities of return with respect to non-GEA areas. The new affordable houses are assigned on the base of gross income and the affordable rent is computed as 30%, that is the threshold defining the lower limit for a household to be rent-burdened. The importance of the program for both constructors and the lowest part of the income distribution has been straighforward, considering the above mentioned 2015 peak due to the expiration of the 421-a. The regime of rent control is splitted in 2 types of measures, supply and demand side. The former are mainly grouded on rent control and rent stabilization. A rent controlled house can be passed to another member
5 - In New York City every newbuilt or restructured dwelling need to comply with the building code and other laws. The certificate of occupancy documents the positive assessment of the compliance with the legislative necessities.
of the family upon the death of the renter under some strict requirements. They account for a very low percentage of the housing supply, usually lower than 2% of the housing stock6.
Instead, rent stabilization covers between 45% and 50% of the existing housing units, a much bigger magnitude. However, the rate showed a decreasing pattern until 2014, when De Blasio was appointed as mayor and started its innovative housing program, “Housing New York: a Five-Borough, Ten-Year Plan” . Rent stabilization consists of a group of measures aimed at constraining the effect of the housing supply and speculation on the rental market. For example, the maximum yearly percentage of rent increase is set by the City government and the renter can have the contract renewed, leaving in his hand the future of the dwelling possession. On the demand side, the most important policy measure are Housing Choice vouchers, that are income based rent subsidies, widely used in the Bronx and Brooklyn. Since 2014, all the quoted measures are part of the Affordable Housing Program, piece de resistance of the electoral program of NYC democratic mayor De Blasio, despite being present also before its implementation. The Program started in 2014 and is aimed at overtuning the housing emergence in NYC, building and, mostly, preserving over than 200.000 affordable houses. The estimated costs were $41,4 billion, making it the most expensive housing program in the history of the City.
3.2 The boroughs7
Staten Island and Queens - Staten Island and Queens are residential boroughs, whose urban scope is to host medium-high income families. Staten Island has a relatively low rate of car free population and the lowest percentage of density around metro stations. Indeed, Staten Island is dislocated far from the other boroughs and the working population is more likely to have their working place in New Jersey or in the West surroundings of New York City. The home-ownership rate are the highest with respect to the other boroughs, implying that Staten Island is less quoted as place to invest or speculate in. The density is the lowest of NYC and the units authorised for new residential buldings are very low, if considered the huge area of Staten Island. Notwithstanding the very high sales volume with respect to the relatively low
6 - The mentioned data and even more specific descriptive statistics on rent stabilization and rent control can be found in the “housing supply reports”, here: http://www.nycrgb.org/html/research/cresearch.html
7 - The borough level analysis is based on the reports available at the following link: http://furmancenter.org/research/sonychan
