Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach

Urban rail transit investments are expensive and irreversible. As people differ with respect to their demand for trips, their value of time, and the types of real estate they live in, such projects are likely to offer heterogeneous benefits to residents of a city. Defining the opening of a major new subway in Seoul as a treatment for apartments close to the new rail stations, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning (ML) approach. This ML approach yields new estimates of these heterogeneous effects. While a majority of the “treated” apartment types appreciate in value, other types decline in value. We cross‐validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines.     

The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

We estimate the cross-sectional dispersions of returns and growth in rents for commercial real estate using data on U.S. metropolitan areas over the sample period 1986 to 2002. The cross-sectional dispersion of returns is a measure of the risk faced by commercial real estate investors. We document that, for apartments, offices, industrial and retail properties, the cross-sectional dispersions are time varying. Interestingly, their time-series fluctuations can be explained by macroeconomic variables such as the term and credit spreads, inflation and the short rate of interest. The cross-sectional dispersions also exhibit an asymmetrically larger response to negative economics shocks, which may be attributable to credit channel effects impacting the availability of external debt financing to commercial real estate investments. Finally, we find a statistically reliable positive relation between commercial real estate returns and their cross-sectional dispersion, suggesting that idiosyncratic fluctuations are priced in the commercial real estate market.     

论我国房地产市场预警系统的建立

建立房地产市场预警系统有助我们在恰当的时候采用正确的房地产宏观调控措施促进房地产市场健康发展。本文将房价收入比、房价租金比、租金收入比、房地产价格增长率与GDP增长率之比、商品房空置率5个指标作为房地产市场预警统计指标,并对它们的预警区间做出界定,然后根据每个指标的统计数据,确定警兆的警级,结合警兆的重要性进行加权综合,综合成一个指数,这样就可以用一组类似于交通信号灯的标志把当前的房地产市场状态直观地表达出来,用以判断房地产市场过热或衰退的程度,来制定房地产宏观调控的方向。     

The Markets for Real Estate Assets and Space: A Conceptual Framework

In this study, we present a simple analytic framework that divides the real estate market into two markets: the market for real estate space and the market for real estate assets. After describing the size and character of flows and stocks in the U.S. real estate market, we use our framework to demonstrate the important connections between the space and asset markets. We illustrate how these real estate markets are affected by the nation's macroeconomy and financial markets, tracing out the impacts resulting from various exogenous shocks on rents, asset prices, construction and the stock of real estate.     

Do Real Estate Prices and Stock Prices Move Together? An International Analysis

The relationship between stock prices and real estate prices has been the subject of substantial debate in both the academic and practitioner literatures. Existing studies have focused on the time series of stock and real estate returns using data from a single country, such as the U.S. By necessity, these studies examine return and price changes over short intervals, creating a bias when property values are smoothed from year to year. Using data from 17 different countries over 14 years, this paper examines the relation between stock returns and changes in property values and rents. Consistent with other country-specific studies, we find that, with the exception of Japan, the contemporaneous relation between yearly real estate price changes and stock returns is not statistically significant. However, when the data are pooled across countries and when we look at longer measurement intervals, a significant relation between stock returns and both rents and value changes becomes apparent. Real estate prices are also found to be significantly influenced by GDP growth rates and provide a good long-term hedge against inflation but a poor year-to-year hedge.     

基于Hedonic模型的小区房价影响因素研究——以南京市鼓楼区为例

从小区房价的微观影响因素出发,分别从建筑特征、邻里特征、区位特征三个方面确定了10个特征价格变量,运用定量和定性相结合的方法,构建影响南京市鼓楼区小区房价的特征价格模型,并对回归结果进行检验和分析。结果表明:建筑年代、容积率、交通干道等级是影响小区房价弹性的最大因素;主要街道等级是影响小区房价的最主要微观因素。希望通过本文研究给房地产开发商、政府、消费者等在决策和选择上提供借鉴。     

Are There Investor Clienteles in Rental Housing?

The possible existence of investor clientele groups has received little attention in the real estate finance literature. In this paper we develop a clientele model, which in equilibrium produces a clustering of investors by tax characteristics. Low-tax-bracket investors are concentrated in low-value rental housing that attracts rents which are high in relation to property values. On the other hand, only high-tax-bracket investors will be observed in high-value rental housing, and they charge rents that are low in relation to property values. An empirical model is specified and estimated using a cross section of investors in Australian private rental housing markets. Investor clienteles are detected among property investors, though there is a weak diversification effect indicating that clientele effects may be stronger among single property investors.     

Commercial Real Estate Rental Index: A Dynamic Panel Data Model Estimation

Using the actual quarterly rental income generated in the years between 2001 and 2010 by over 9,000 NCREIF commercial properties, we construct a commercial real estate rental index and estimate the time series properties (e.g., mean‐reversion speed and volatility) of market‐wide rental growth using a dynamic panel data model. The dynamic panel data model has several advantages over a standard hedonic regression. In addition, we incorporate age effects into our panel data model, and by doing so we correct the age bias in the repeated sales method and in the simple average method. Our estimates show that rental growth is cyclical but it generally lags behind broader economic growth. Surprisingly, the long‐term average rental growth is significantly lower than what is usually perceived, and the volatility of rental growth can be significantly under estimated when the conventional methods are adopted. We also find significant cross‐property type and cross‐region variations in the rental adjustment process. In contrast to the existing literature, we find a strong negative relation between rental growth and cap rate, and that this relation is significantly stronger than that between NOI growth and cap rate. Finally, we establish an empirical relation between price return and rental growth in the commercial real estate market.     

Space-Varying Regression Coefficients: A Semi-parametric Approach Applied to Real Estate Markets

This paper presents a method for estimating home values by non-parametrically incorporating the physical location of the properties. Specifically, I allow the parameters of the observed covariates to vary in space. This approach mitigates one of the biggest deficiencies inherent in hedonic pricing models–omitted variables. I demonstrate the advantages of the proposed method using real estate transaction data from Los Angeles County. The estimation finds a substantial spatial variation of the marginal values of the hedonic characteristics and provides an insight into the segmentation of the market. The proposed method is an extension of semi-parametric multi-dimensional k-nearest-neighbor smoothing. It alleviates a fundamental problem known as the curse of dimensionality by incorporating parametric components into a non-parametric estimation.     

Chicago's Office Market: Price Indices, Location and Time

Conventional wisdom holds that overbuilding and high vacancy, coupled with curtailed tax benefits, have led to reduced office property values since the late 1980s. Yet assertions that office real estate values fell between the mid-1980s and mid-1990s are not supported everywhere by convincing evidence. This study offers a hedonic analysis of Chicago area office properties that sold from 1986 through 1993. Whereas earlier office market studies generally have been based on rents, this study focuses on variation in actual sale prices (although the prices were not adjusted for financing differences). The transaction-based index estimated here does not support the existence of a nominal office property price level decline beginning in the mid-to-late 1980s. In fact, the results show an upward trend in office property values after 1986, with nominal declines in office market price levels occurring only in the latter portion of the study period.     

Real Estate Values, Federal Income Taxation, and the Importance of Local Market Conditions

This article develops an income property valuation model that explicitly incorporates the effects of local market conditions. In particular, the model allows real rents to respond to a change in tax law, or to any exogenous shock to the system, with the dynamics of the rent change over time dependent upon current local supply and demand conditions and on the expected rate of economic growth in the local economy. Application of the model to an analysis of reduced capital gain taxation demonstrates that a partial reversal of TRA86's real estate provisions would disproportionately benefit those markets, and related lending institutions, which are most in need of a bolstering of real estate values.     

Hedonic Models with Redevelopment Options under Uncertainty

In the hedonic model, implicit market prices can be interpreted as the present values of rents per unit of each hedonic characteristic. But when rents rise, there may be substantial value associated with the option to redevelop to higher intensity per unit land value. In the presence of option value, we first demonstrate that hedonic linear regressions should include an additive nonnegative term for the value of the option. This term increases in the variance of the underlying stochastic process. If this term is omitted, then estimates of implicit market prices for desirable (undesirable) characteristics will be biased downward (upward). This prediction is supported by recent empirical studies. We further suggest that future empirical work can employ the nonlinear functional form derived from our theory.     

Determinants of the Rate of Return for Nonresidential Real Estate: Inflation Expectations and Market Adjustment Lags

This paper analyzes the economic forces that determine the real rate of return for nonresidential real estate. Our analysis shows that the intermarket variation in the real rate of return is statistically significant, and the rate of return differs by land use and market area, as well as over time in response to changes in macro-economic conditions. We use inflation variables as surrogates for changes in macroeconomic conditions over time. In contrast to earlier studies, we find that nonresidential real estate may not outperform expected inflation. We believe that the impact of expected inflation (and other macroeconomic variables) on real estate rates of return depends upon the interaction of the macro-environment and specific local real estate market conditions. Finally, our empirical evidence suggests that the effects of a given shock dissipate rather quickly in most markets when we take into account locational and property use differences.     

Estimating Homeownership Costs: Owners' Estimates of Implicit Rents and the Relative Importance of Rental Equivalence in the Consumer Price Index

This paper discusses the relative importance of Rental Equivalence (REQ) in the U.S. Consumer Price Index. A hedonic model of rents is estimated, and the resulting parameters are then used to compare hedonic estimates of implicit rents to homeowner estimates like those actually used to estimate the REQ expenditure weight. There are several reasons why hedonic estimates might be below actual implicit rents, and these effects are controlled for in the model. Owner estimates of implicit rents are then shown to be comparable to such hedonic estimates. Results support the method used to estimate the expenditure weight.     

The Gender Gap in Real Estate Sales: Negotiation Skill or Agent Selection?

This study examines differences in net selling price for residential real estate across male and female agents. A sample of 2,020 home sales transactions from Fulton County, Georgia, are analyzed in a two‐stage least squares, geospatial autoregressive corrected, semi‐log hedonic model to test for gender and gender selection effects. Although agent gender seems to play a role in naïve models, its role becomes inconclusive as variables controlling for possible price and time on market expectations of the buyers and sellers are introduced to the models. Clear differences in real estate sales prices, time on market and agent incomes across genders are unlikely due to differences in negotiation performance between genders or the mix of genders in a two‐agent negotiation. The evidence suggests an interesting alternative to agent performance: that buyers and sellers with different reservation price and time on market expectations, such as those selling foreclosure homes, tend to select agents along gender lines.     

Economic Depreciation of Residential Real Estate: Microlevel Space and Time Analysis

Three elements in the study of real estate depreciation that warrant further consideration are uncovered: the spatial variation of depreciation on a micro scale, the variability of depreciation within a single market across time and the recognition of land value as an influence in modeling real property prices. Taken together, these three dimensions provide an opportunity to further expand the understanding of residential economic depreciation while enhancing the predictive power of real estate market models. The analytical results, utilizing a land-value-adjusted hedonic model, indicate that both the intramarket location and the year in which the property sold have significant impacts on the observed rate of economic depreciation. Such information is vitally important to policymakers and others interested in accurate modeling of real estate markets.     

Rental Expectations and the Term Structure of Lease Rates

We consider the term structure of lease rates in a general setting where both rents and interest rates are stochastic. The framework is applicable to any leasing market, but we focus on real estate. We find that the ``expectations hypothesis", that is, forward rates are unbiased estimators of future rents, requires similar assumptions as in interest rate theory to hold. To study bias magnitude, simulations are performed using a parameterization of the general framework. Different realistic values for risk aversion and interest rate stochastics can generate widely different shapes of the term structure, holding objective expectations constant. Thus an expected increase in rent is consistent with a downward-sloping term structure and vice versa.     

Testing machine learning systems in real estate

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.     

Explaining the Variability of Apartment Rents

The research reported here uses regression analysis to analyze rent variations in a sample of apartment data from the Phoenix metropolitan area. Many of the variables used in hedonic price studies of houses are found to be significant in explaining variations in apartment rent. There are differences between hedonic studies of houses and apartments particularly with respect to common area features or amenities. The analysis of various submarkets also produced interesting results. Various uses can be made of the results of this and similar studies by appraisers (market-derived adjustments), property managers (setting rents) and feasibility analysts (the design of apartment projects).     

Correlation Dynamics and Determinants in International Securitized Real Estate Markets

We find the correlation movements among eight developed securitized real estate markets and among their stock markets are quite synchronized over the period from 1995 through 2012. There is a high degree of correlation dependence with many of the realized correlation series subject to regime switching. Moreover, international correlations of public property returns could be significantly explained by five real estate variables that include global real estate securities market volatility, co‐existence of real estate investment trust (REIT) influence, underlying direct real estate return performance differential, real estate securities volatility differential and real estate securities market size differential after controlling for macroeconomic influence and stock market effect. The importance of the control and real estate variables in explaining the return correlations varies across the economies examined.