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Abstract: We present the first capitalization study to look at crime fighting and house prices using the causal inference technique of regression discontinuity. It is also the first study on the link between police spending and housing sales volume. Voting to increase police taxes and spending by 15% is not linked to house prices or transaction volume overall. However, increasing spending causes 13% higher housing prices in low-income cities and at least a 14% decrease in house prices in high-income cities. The effects persist through all five years after the vote that we study. The sale price results are most consistent with overfunded police departments, rather than Tiebout sorting, neighborhood instability, or signaling. Our results suggest that the small or non-existent link between house prices and crime found by the literature really just reflects the sum of large but opposite moves in house prices in different market segments. PubDate: 2025-04-16
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Abstract: Real Estate Investment Trust (REIT) returns and volatility have been extensively studied, yet typically in isolation from each other. Given that returns and volatility are generally connected in the eyes of investors, we simultaneously analyze the drivers of REIT returns and volatility over the modern REIT era (1991–2022) using an eXtreme Gradient Boosting (XGBoost) machine learning algorithm. We enhance transparency and utility through the application of explainable artificial intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE), which unpack the decision-making process of the model. Our analysis reveals that while no single feature consistently dominates, the influence of various drivers fluctuates significantly over time. Notably, the importance of macroeconomic indicators generally diminishes, while REIT-specific characteristics become more influential during the sample period. Furthermore, market cycles (macroeconomic shocks) cause large deviations from otherwise long-run patterns. However, during these times of economic uncertainty, drivers of risk and return correlate more strongly in comparison to times of economic stability. Lastly, we find non-linearities in the way the drivers influence returns and volatility. These insights have significant implications for investors, policymakers, and researchers as they navigate the evolving landscape of real estate investments. PubDate: 2025-04-01
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Abstract: This study investigates the relationship between natural disaster risk and capitalization rates (cap rates) in the U.S. commercial real estate market, focusing on the role of climate change beliefs in shaping this relationship. Utilizing a comprehensive dataset of over 4,800 single-tenant, net lease property transactions across the United States from 2014 to 2019, we combine property-level data with the Federal Emergency Management Agency (FEMA) National Risk Index and Yale Climate Opinions Map survey scores to examine how the pricing of disaster risk varies based on local climate change beliefs. Our findings suggest that higher disaster risk is associated with higher cap rates, with the effect being significantly more pronounced in areas with a stronger belief in climate change. We further explore the nuances in climate change beliefs, the role of political orientation, education, and age in shaping these beliefs, and the impact of recent exposure to natural disasters on the pricing of disaster risk. Our study contributes to the understanding of how natural disaster risk is incorporated into the pricing of commercial real estate and highlights the importance of considering behavioral and perceptual factors in the analysis of environmental risks and asset prices. PubDate: 2025-03-23
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Abstract: Using a transaction-level dataset of presale private properties in Singapore over 24 years, this paper investigates the property price dynamics following project launches. I show that for a newly launched residential project, presale prices increase by approximately 0.9% to 1.5% every 100 days following the launch date, indicating a pattern of IPO underpricing. The price appreciation trend becomes more pronounced as financing costs increase. By matching transaction data with developer information, I demonstrate that developers tend to underprice their first two presale projects and then adjust pricing strategies in subsequent projects. Developers can learn pricing strategies from their experience and adjacent peers to avoid losses. PubDate: 2025-03-13
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Abstract: Reverse mortgages, as an important means to provide financial resources to elderly homeowners, have received considerable attention from researchers, practitioners, and policymakers. The role of a secondary market, however, has been largely overlooked in the literature. This paper seeks to bridge this gap by delving into the intricate design of the Home Equity Conversion Mortgage (HECM)-backed securities (HMBS). We examine the cash flow patterns and financial performance of key stakeholders, including HECM issuers, HMBS investors, and the Federal Housing Administration (FHA), in this mechanism. Moreover, we stress the importance of incorporating loan assignment risk and adopting ruin-based risk measures to assess the financial viability of the HECM program. We find that neglecting assignment risk may lead to significant underestimation of the FHA’s ruin probability and capital requirements. Our results also suggest that traditional risk measures, such as Value-at-Risk (VaR), may underestimate capital requirement for the FHA compared to ruin-based risk measures. Our findings hold important policy implications for the FHA, particularly regarding its mortgage insurance premium structure, cash draw restrictions, collateral risk assessment, and capital requirements. Additionally, they provide valuable guidance for HECM issuers and HMBS investors to evaluate their risk-return profiles. PubDate: 2025-03-05
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Abstract: Policy makers and researchers are interested in the factors driving gentrification, a process often associated with the displacement of lower-income residents by higher-income, more educated households. This study examines one potential factor: the influx of same-sex couples into a community. Anecdotal evidence suggests there is a connection between the presence of same-sex couples and gentrification. We empirically investigate this relationship using both least-squares methods and an instrumental variables strategy. In our instrumental variables approach, we use voting results for the state-level equivalent of the Defense of Marriage Act in Ohio as an instrument for changes in the number of same-sex couples. Our findings consistently demonstrate that areas experiencing a larger increase in same-sex couples are more likely to undergo gentrification. Furthermore, semiparametric analysis reveals a tipping point beyond which gentrification becomes significantly more likely, suggesting that the clustering of same-sex couples contributes to neighborhood change. PubDate: 2025-02-28
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Abstract: Much of the debate over whether fund managers attempt to subvert the evaluation procedure to their advantage has focused on private equity buyout funds. This study provides new evidence that bears directly on this ongoing debate by investigating private equity funds that invest in commercial real estate. Three key questions motivate the exercise in what follows. First, given the subjective nature of the evaluation process, can the decisions made by private equity real estate fund managers shape the outcome of property performance into something that affects the fund manager’s fee' Specifically, are unlevered deal level alphas “known” at acquisition with enough certainty that the manager can utilize positive financial leverage to enhance Jensen’s alphas' Second, do discrepancies in reported versus true deal-level performance exist in booming versus declining markets' Third, it is not entirely clear as to which type of private equity real estate fund, core, value-added, or opportunistic, poses relatively more moral hazard than others. The theory would say that value-added and opportunistic funds pose the biggest threats, but there is a growing concern of style creep and style gaming among core funds. We find that for a vast majority of property deals over the sample period of 1978 through 2009, particularly for properties that were acquired prior to 2001, Jensen’s alphas exceed the unlevered deal-level alphas by a wide margin, with a range of approximately 0.40 to 8.90%. Our results also suggest that years of high Jensen’s alphas are followed by years of low Jensen’s alphas that are well below true deal-level alphas. The latter is understandable in light of the fact that fund managers use leverage to increase their potential returns but at the cost of more risk. PubDate: 2025-01-30
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Abstract: Crime is a disamenity, so buyers should be willing to pay more for a house (all else equal) in a low crime area, suggesting that high crime rates depress housing prices. Conversely, it is plausible that criminals prefer wealthier areas because of the higher expected returns from their transgressions. This study examines the link between measures of crime and prices of residential housing. Our data begin in 2008 and end in 2020 for Seattle, Washington, including all reported felonies (756,304); 911 calls (1,528,303); all recorded residential real estate transactions (61,902 after filtering), as well as the corresponding property characteristics; demographic data and the associated changes. The inherent endogeneity between crime rates and housing prices forces us to find an instrument for crime rates. After rejecting several plausible choices, based on the Wu-Hausman test, the key variable in our empirical analysis is the number of 911 calls (contemporaneous and lagged) in a given beat. Our results present somewhat mixed evidence on the impact of crime on housing prices. Without adjustment for spatial autocorrelation, a 1 percentage point increase in crime rates (instrumented by the number of 911 calls) leads to approximately a 0.55% decrease in house prices. However adjusting for spatial autocorrelation changes this figure to a 0.80% increase. We further show that distance to a crime hotspot is significantly negatively related to housing prices, suggesting that criminals choose to operate in wealthier areas, which is consistent with our findings incorporating spatial autocorrelation. PubDate: 2025-01-30
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Abstract: Is the formation of investor networks associated with superior fund performance' Our analysis of more than 2,000 private equity real estate (PERE) funds over three decades reveals abnormal performance among PERE funds dominated by institutional investor cliques. Specifically, investor cliques with a more extensive history of joint investment are associated with superior fund performance that is not explained by common fund characteristics. Such correlation is not predicted by a simple Bayesian update without access to private information, implying that investor networks may be a channel through which limited partners identify and access better-performing fund managers. We also provide evidence that Limited Partner (LP) networks are not a mere result of rational herding by showing that the clique-level Herfindahl–Hirschman Index for investor AUM does not positively predict greater abnormal return. We further observe that investor cliques prolong the time needed to reach maximal commitment. As General Partners (GP) are known to utilize a subscription line of credit to borrow against committed capital to boost return, we infer more financing discretion granted to GPs by cliques. PubDate: 2025-01-21
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Abstract: Situated atop condominium buildings, ‘top floor units’ (TFU) offer unparalleled views and privacy, courtesy of accessible roofs. This paper empirically examines this status symbol and finds that: (1) TFUs interact with the macroeconomy differently from ordinary units, (2) when considering the liquidity factor, TFUs should not be included in the portfolio, (3) the trade-off between holding period and annualized return for TFUs significantly differs from ordinary units, suggesting alternative investment strategies are employed for TFUs, and (4) the liquidity of the TFU segment is less stable than ordinary units, potentially deterring short-term speculators. PubDate: 2025-01-19
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Abstract: We examine whether property market liquidity impacts the choice between secured and unsecured debt. A sample of real estate investment trusts (REITs) allows us to estimate the market liquidity of a REIT’s underlying assets and the debt secured by those assets (or unsecured). Using an instrumental variables approach, we find a positive relationship between a REIT’s property market liquidity and its use of unsecured debt relative to secured debt - when a REIT has greater exposure to more liquid underlying property markets, it is more likely to rely on unsecured debt. We investigate several aspects of this relationship including the debt level, issuances, and property loan-to-value ratio. In each case, we find support for our main result. Likewise, our results are robust to (a) using alternative instruments; (b) controlling for REITs’ unencumbered assets, as well as asset quality and redeployability; (c) controlling for credit market conditions; (d) accounting for real estate market conditions; (e) excluding firms that focus on residential real estate; and (f) adding stock market liquidity. Our study highlights the importance of property market liquidity in the debt structure of REITs. PubDate: 2025-01-04
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Abstract: This is the first study to analyze REIT Net Asset Value analyst coverage and dispersion. We find that NAV analyst coverage has a positive relationship with REIT value and a negative relationship with REIT volatility. Subsequently we analyze NAV analyst estimate dispersion and find that it has a positive relationship with REIT leverage and volatility. We break down our sample by property type and find that retail REITs have the greatest NAV coverage and hospitality REITs have the greatest NAV analyst dispersion. Finally, we compare the significance of NAV forecast dispersion to earnings (FFO) forecast dispersion. We find that NAV dispersion has a significant negative relationship with REIT value whereas FFO dispersion is not found to have a significant relationship. PubDate: 2024-12-30
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Abstract: Owners of real assets often have informational advantages over other investors about asset- specific cash flows and local market conditions. When local property values suddenly decline, these investors may have to sell their assets quickly and have less access to credit due to the decline in the value of their collateral. This opens up the market to outside investors who lack the same informational advantages but have better access to capital. We find that, in times of stress, the volume of transactions in hard-hit markets falls, and entry from out-of-market buyers increases. Out-of-market buyers consistently pay less for properties, by about 20 basis points in terms of a cap rate, during both boom and bust markets, which is consistent with them reducing their bids when they are at an informational disadvantage. Out-of-market investors also tend to generate higher holding period returns for the previous owners, suggesting that they may still be overbidding for properties. Out-of-market buyers are less likely to purchase properties where they have a significant information disadvantage, such as troubled properties during a market decline. PubDate: 2024-12-27
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Abstract: We use parallel processing and improved algorithms to search for the weights given to two locational coordinates as well as three non-locational dimensions to find multidimensional neighbors (previous in time) to fit a multidimensional STAR model. We find that the improvements allow for quick specification searches. The effect of the improved specifications is a material reduction in the standard deviation of the residuals. PubDate: 2024-12-20
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Abstract: The co-movement of house prices across different regional markets has important implications for portfolio management, the conduct of monetary policy and labor mobility. Previous research examining several decades of co-movement has found price cohesion between US regions rising steadily from the mid-1990s or mid-2000s. Using a more recent data set, and applying more recently developed filters than previous studies employed, we find instead that by one filter cyclical co-movement has dropped since the early 1980s, and although it has fluctuated has displayed no sustained upward trend since. A second filter leads to a measure showing that co-movement did drop in the mid-1980s, and recovered in the mid-1990s. However, this recovery has not been steady, and has been marked by a number of declines in cohesion. Indeed, both filters show a drop in cohesion over the covid quarters and an increase in the variation between regions regarding how closely housing markets co-move. This was due to more extreme dispersion in house price movements across regions compared with what had prevailed in previous years. We note that the Pacific region, which had the fastest-growing prices in the US since the 1970s, grew the least of any market after the first year of the pandemic, while lower-cost regions experienced more rapid price increases during this period. These results are consistent with studies which have found covid led to a “flattening” of the gradient between neighborhoods with high and low-cost housing. We find some evidence that regions with a higher percentage of their workforces working-from-home experienced lower house price growth relative to other regions post-pandemic. PubDate: 2024-11-01
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Abstract: This study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders. PubDate: 2024-10-25
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Abstract: We study the transition in and out of homeownership during the 2008 housing market bust, using detailed micro-level data covering the entire Danish population. We document that homeownership decisions for certain groups of households are cyclical. Households which are affected more by falling house prices reduced their likelihood of acquiring a home during the bust more than other households. These households are characterized by lower levels of net worth, lower income, less education, are singles, and younger. Combined with younger households abandoning homeownership more during the bust, the bust contributed to a significant inter-generational shift in homeownership from younger to older households. PubDate: 2024-10-21
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Abstract: The literature on the use of machine learning (ML) models for the estimation of real estate prices is increasing at a high rate. However, the black-box nature of the proposed models hinders their adoption by market players such as appraisers, assessors, mortgage lenders, fund managers, real estate agents or investors. Explaining the outputs of those ML models can thus boost their adoption by these domain-field experts. However, very few studies in the literature focus on exploiting the transparency of eXplainable Artificial Intelligence (XAI) approaches in this context. This paper fills this research gap and presents an experiment on the French real estate market using ML models coupled with Shapley values to explain the models. The used dataset contains 1,505,033 transactions (in 7 years) from nine major French cities. All the processing steps for preparing, building, and explaining the ML models are presented in a transparent way. At a global level, beyond the predictive capacity of the models, the results show the similarities and the differences between these nine real estate submarkets in terms of the most important predictors of property prices (e.g., living area, land area, location variables, number of dwellings in a condominium), trends over years, the differences between the markets of apartments and houses, and the impact of sales before completion. At the local level, the results show how one can easily interpret and evaluate the contribution of each feature value for any single prediction, thereby providing essential support for the understanding and adoption by domain-field experts. The results are discussed with respect to the existing literature in the real estate field, and many future research avenues are proposed. PubDate: 2024-09-19
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Abstract: This article investigates measurement errors when using indices to model house prices over time. Our analysis, comparing index prices to actual transaction values, reveals that in many cases, widely-used indices display measurement errors correlated with the index values. Measurement error correlated with predictors constitutes “differential measurement error” at the level of the data generating process (DGP). We further explore the presence of differential measurement error within the context of mortgage lending. Our findings uncover substantial measurement errors in mortgage data, which not only diminish the predictive accuracy of models but also introduce notable biases in the coefficient estimates of variables. PubDate: 2024-09-11
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Abstract: This paper is the first to empirically investigate the spillover effects of land value taxation. Using rich panel data for municipalities in Pennsylvania over the period 1980–2010, this study extends the existing research by offering the first evidence on the external impacts of land value taxation as well as the spatial dynamics of these impacts. The empirical model separately identifies the spillover effects of land value taxation and the externalities associated with traditional property taxation. The study shows that taxing land at a higher rate than structures on the land slows down employment growth in close neighbors but speeds up employment growth in neighbors within a longer distance. The findings suggest that land value taxation generates differential spillover effects across space. The paper discusses two underlying effects behind the observed differential impacts and opens up new avenues for further research. PubDate: 2024-09-05
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