Case-Shiller Home Price Index Research Articles

Property investor decisions using income and rental ratio signals

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Hedging housing price risks: some empirical evidence from the US

We analyze household hedging costs and market liquidity of exchange traded options on a set of well-developed U.S. home price indexes allowing homeowners to hedge the downside risk of housing prices. We estimate empirically the impact of hedging costs on market liquidity of housing derivatives using prices from Case–Shiller Home Price Index (CSI) futures options and Barone-Adesi and Whaley [Efficient analytic approximation of American option values. J. Finance, 1987, 42, 301–320] simulations. We find that hedging costs significantly affect household savings resulting from hedging. We propose a new cost-based illiquidity measure for housing derivatives and link it with traditional contract-based liquidity measures in thinly traded derivatives markets. We document a negative relation between savings from hedging and our cost-based illiquidity measure. We further perform a series of robustness checks. Overall we suggest that the liquidity of exchange traded housing derivatives could benefit U.S. homeowners.

International Real Estate Review

We investigate the profitability of momentum strategies in the market for single-family homes by using 10 city-level Case-Shiller home price indices (HPIs). Compared with the momentum strategies based on the Fama-French 10-industry portfolios of stocks, the profits from the single-family HPIs are more statistically significant, less sensitive to the construction methods of the momentum strategies and more correlated across different strategies. The momentum profits from the HPIs tend to be counter-cyclical, unlike the pro-cyclical behaviors of the momentum profits from stock portfolios. The differences in the momentum profits with HPIs and stocks indicate that a momentum strategy with the former can help diversify the risk in the asset portfolio of investors.

Mortgage recourse provisions and housing prices

In light of the large swings in housing prices in the United States in recent years, there has been considerable interest in trying to understand the various factors which led to the boom and bust of the housing market. In this paper, we explore the impact of the legal environment from provisions for mortgage default across U.S. states. To do so, we develop a rigorous framework with microeconomic foundations for financial intermediaries. To be specific, we introduce a housing market and a residential mortgage market in the Diamond and Dybvig framework which emphasizes the role of depository institutions to help depositors manage idiosyncratic liquidity risk. Notably, we think of non-recourse provisions as a legal arrangement to protect risk-averse homeowners from the loss of housing value. While housing demand should be higher in markets where mortgage borrowers have full insurance, lenders also adjust the amount of mortgage credit provided to protect their risk-averse depositors. Thus, a priori, there is not an obvious connection between mortgage recourse provisions and housing prices. To draw further insights into the issue, we proceed to look at empirical evidence on housing prices at the MSA-level using the Case-Shiller Home Price Index. Once one controls for regional level unobservables, the evidence suggests that the demand side factors dominate in which prices are higher in non-recourse states, following the prediction from the model that the demand for mortgages would be higher. We next move to obtain more concrete predictions from our theoretical framework with calibration exercises to study the effects of mortgage recourse. Upon calibrating the model to match some stylized evidence on housing market conditions, the theoretical predictions are consistent with the regression analysis. In this manner, our work sheds numerous insights into the implications of the legal landscape regarding mortgage default for housing market activity.

Home Price Index: A Machine Learning Methodology

Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.

Asset Pricing with Spatial Interaction

We propose a spatial capital asset pricing model and a spatial arbitrage pricing theory (S-APT) that extend the classical asset pricing models by incorporating spatial interaction. We then apply the S-APT to study the comovements of eurozone stock indices (by extending the Fama–French factor model to regional stock indices) and the futures contracts on S&P/Case–Shiller Home Price Indices; in both cases, spatial interaction is significant and plays an important role in explaining cross-sectional correlation.The e-companion is available at https://doi.org/10.1287/mnsc.2016.2627 .This paper was accepted by Neng Wang, finance.

An assessment of the risk and return of residential real estate

Purpose – The home is a substantial investment for most individual investors but the assessment of risk and return of residential real estate has not been well explored yet. The existing real estate pricing literature using a CAPM-based model generally suggests very low risk and unexplained excess returns. However, many academics suggest the residential real estate market is unique and standard asset pricing models may not fully capture the risk associated with the housing market. The purpose of this paper is to extend the asset pricing literature on residential real estate by providing improved CAPM estimates of risk and required return. Design/methodology/approach – The improvements include the use of a levered β which captures the leverage risk and Lin and Vandell (2007) Time on Market risk premium which captures the additional liquidity risk of residential real estate. Findings – In addition to presenting palatable risk and return estimates for a national real estate index, the results of this paper suggest the risk and return characteristics of multiple cities tracked by the Case Shiller Home Price Index are distinct. Originality/value – The results show higher estimates of risk and required return levels than previous research, which is more consistent with the academic expectation that housing performs between stocks and bonds. In contrast to most previous studies, the authors find residential real estate underperforms based on risk, using standard financial models.

A Note on REIT Returns and the Case-Shiller Home Price Index

In this note, we examine the relationship between real estate investment trust (REIT) returns and the Case-Shiller Home Price Index using panel data on 159 unique REITs over the 25-year period of 1...

Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data

House sales are determined based on the Standard & Poor’s Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction.

Credit Fluctuations and Lodging Firms: An Investigation of the Differing Capital Structures in the U.S. Lodging Industry

In the area of corporate finance, decisions regarding the constituents of overall capital structure are the most critical. These financing decisions are even more critical to lodging firms because of the unique nature of the industry. This study empirically investigates the effect of credit availability on the leverage of the lodging industry in the U.S. using multivariate analysis of variance. Three time points of differing credit availability (low, high, and average) were identified using the Case-Shiller home price index. Leverage, net leverage, and short-to-long-term debt ratios of large and small U.S. lodging firms were analyzed at these differing credit availability time points to assess any significant differences. Significant effects of credit availability were found on the leverage and net leverage of lodging firms, but no significant effect was found on the short- to long-term debt ratio of U.S. lodging firms. Interestingly, the leverage levels were found to be highest at the average availability of credit than when compared to the high and low availability of credit.

Floriculture Crops Economic Outlook for 2014

This 15-page report summarizes industry statistics using data from primary and secondary sources and highlights production and sales trends in the US environmental horticulture industry in 2014. Data sources include the United States Department of Agriculture National Agricultural Statistics Service (USDA/NASS), US Census Bureau, the IBIS World Industry Reports, National Association of Home Builders, S&P/Case-Shiller Home Price Indices, US Bureau of Labor Statistics, University of Florida Bureau of Economic and Business Research, AIA Economics and Market Research Group, and Florida Realtors®. Primary data is collected through the National Nursery Survey, conducted by the Green Industry Research Consortium. Written by Hayk Khachatryan, Alan W. Hodges, and Shawn Steed, and published by the UF Department of Food and Resource Economics, March 2014.
 FE941/FE941: Floriculture Crops Economic Outlook for 2014 (ufl.edu)

Expected return, time-varying risk, and hedging demand in the US REITs market

This paper explores the time-varying relationship between the return and risk for the portfolios in the US real estate investment trusts (REITs) market. Three classes of REITs portfolios are sorted on size, momentum and book-to-market. The empirical results place a positive trade-off between the return and predictable covariation in all REITs portfolios. Furthermore, this paper examines the significance of intertemporal hedging demand in each REITs portfolio by extending the Intertemporal Capital Asset Pricing model (ICAPM) with a set of prevailing macroeconomic variables and financial market indicators, which indicate that the predictable movements in return could be attributed to the change in covariances with these innovations. The conclusive results show that innovations in inflation rate, de-trended short-term interest rate, Fama-French momentum factor, S&P/Case-Shiller home price index, and Barclay Capital long-term government/corporate bond index play a crucial role in hedging demand for REITs portfolios.

Финансы и идеальное государство (перевод Ю. Каптуревского)

Robert J. Shiller is the Professor of Economics at Yale University and the co-creator of the Standard& Poor’s Case-Shiller Home Price Indices. His book is devoted to financial capitalism, implying a system in which finance has taken the lead as the engine driving capitalism. The author is mostly interested in searching an answer to the question how we can make financial institutions work better for all of us. To achieve this purpose, Shiller develops a theory of financial capitalism in which finance is defined as the science of goal architecture. It is proposed that finance can contribute to success achievement and construction of the good society. The presented book by Shiller describes realities of the modern financial capitalism and proposes the critical reflections on it. The author also tries to relief the tension between human values and the issue of welfare concentration.The journal “Economic Sociology” publishes an excerpt of this book- “Introduction: Finance, Stewardship, and Our Goals”, in which the author problematizes the public hostility toward financial capitalism and seemingly profound contradiction between finance and the good society. The author demonstrates what kind of distinctive treats financial capitalism gets after the information age has come. Finally, Shiller proposes a working theory of financial capitalism.

A THREE-STEP COMBINED GENETIC PROGRAMMING AND NEURAL NETWORKS METHOD OF FORECASTING THE S&P/CASE-SHILLER HOME PRICE INDEX

Forecasts of the San Diego and San Francisco S&P/Case-Shiller Home Price Indices through December 2012 are obtained using a multi-agent system that utilizes January, 2002–June, 2011 data. Agents employ genetic programming (GP) and neural networks (NN) in a three-stage process to produce fits and forecasts. First, GP and NN compete to provide independent predictions. In the second stage, they cooperate by fitting the first-stage competitor's residuals. Outputs from the first two stages then become inputs to produce two final GP and NN outputs. The NN output from the third stage using the combined method produces improved forecasts over the 3-stage GP method as well as those produced by either method alone. The proposed methodology serves as an example of how combining more than one estimation/forecasting technique may lead to more accurate forecasts.

Diminishing Margins: Housing Market Declines and Family Financial Responses

We utilize data from the Panel Study of Income Dynamics (PSID) to study borrowing decisions and other factors related to the run-up in housing prices in 1999-2007, their precipitous decline in 2007-2009, and how they contributed to mortgage distress and foreclosures as of 2009-2011. Difficulties were concentrated in selected real estate markets where the Case Shiller home index declined more than 35% from 2007 to 2009. Often expecting further price appreciation or responding to a positive family labor market and income circumstance, homeowners, supported by their lenders, allocated too much of their family income to support house payments and put themselves in a risky position. The year of taking the original mortgage, the rate of decrease in the Case-Shiller home price index, household wealth, and labor market and disability status are substantial predictors of mortgage payment distress and foreclosure.

From Macro to Micro (and Back Again)

Like the broad economy, the U.S. housing market is making a slow but steady recovery. Over the past few years, housing inventory has been reduced. In some areas, the market is even tightening and prices are rising again. Given these trends, investors are beginning to dip their toes cautiously back into real estate. Further, a new exchange-traded futures contract on the Chicago Board Options Exchange (CBOE) can help them hedge their exposures. Two key house-price indices released in August 2012 indicate improvement in the prospects for residential real estate. The Freddie Mac House Price Index showed a 4.8% gain from March to June 2012, the largest quarterly pickup in eight years, and a June-to-June rise of 1% (the largest annual appreciation since November 2006). Moreover, data released by S&P/Case-Shiller Home Price Indices revealed that its three headline composites ended the second quarter of 2012 with positive annual growth

The Efficiency of the Market for Single-Family Homes: A Critique on Case and Shiller (1989)

This paper comments on the Weighted Repeated Sales (WRS) method in Case and Shiller (1989). We find that Case-Shiller’s model for step-two of WRS is conceptually mis-specified and empirically inaccurate, which are likely to cause the S&P/Case-Shiller Home Price Indices to be biased for the most critical housing markets (i.e. nine of the ten cities with housing related derivatives based on the indices). Based on our examination of real estate market risks, we propose a quadratic model for the second step of WRS and show that our specification is actually more consistent with the findings by Case and Shiller (1989).

The Rent-Price Index in U.S Housing Markets

In this paper, the relationship between house prices and rents across eight major metropolitan areas in the U.S. is examined, and a rent price index to provide the framework of our analysis is developed. Adjustments in the rent-price ratio can theoretically occur through the numerator, the denominator, or both. Intuitively, rents are stickier than prices, and therefore, prices are expected to do most of the adjusting when the ratio significantly deviates from historical norms. Our results support this intuition. Our analysis shows that low rent-price ratios are associated with subsequent periods of low or negative house price changes. This implies that house prices overshoot fundamental values associated with capitalization of future rents, and revert to equilibrium through subsequent price correction. Based on rent-price index levels across the eight regions we analyzed, the current housing market correction has already unwound much of its previous overshooting, but it is important to note that the overshooting process may work to the downside as well as the upside. For house price data, we utilize the S&P/Case-Shiller Home Price Indices. For rent data, we utilize the Bureau of Labor Statistic's metro area owner's equivalent rent indices, an input to the Consumer Price Index (CPI).

On the Price Comovement of U.S. Residential Real Estate Markets

This study addresses the recent performance of the U.S. residential real estate markets. We investigate the comovement among Case-Shiller Home Price Indices for 14 metropolitan areas between 1992 and 2008. We identify the portion of this comovement deemed as fundamental (excessive), which we define as the covariation that can (cannot) be attributed to common fundamental factors directly influencing real estate prices. We find that the degree of comovement in these markets increased over the sample period, most significantly so in the late 1990s, but that this increase is largely due to systematic sources of risk; the degree of excess comovement is a less important factor. Further analysis indicates that the dynamics of comovement among metropolitan U.S. residential real estate markets within the sample period are mostly attributable to underlying systematic real and financial factors, consistent with a greater fundamental integration of those markets. We discuss the implications of these results for the evolution of U.S. real estate prices over the last two decades and the ongoing credit crisis.

International Real Estate Review

This paper applies the Hodrck-Prescott (HP) filter to forecast short-term residential real estate prices under cyclical movements. We separate the trend component from the cyclical component. We show that each regional residential market reacts not only to previous price movements, but also that these regional markets react to previous shocks under Auto Regressive Integrated Moving Average (ARIMA) modeling. Using the S&P Case-Shiller Home Price Index, we compare our forecast to index values from the Chicago Mercantile Exchange (CME) Housing Futures and Options. Our study identifies possible systematic errors from the different price adjustments reflecting current market situations.