The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales,Hedonic-Regression,and Hybrid Approaches

This article examines a number of hypotheses that underpin the repeat-sales and hedonic approaches to the construction of housing price indices, as well as the practical problems associated with the implementation of either approach. We also examine a hybrid procedure that combines elements of both the repeat-sales and hedonic-regression techniques. For our sample of individual home sales in Oakland and Fremont California over an 18-year period, repeat-sales methods are subject to sample selection bias; the maintained assumption of time constancy of implicit prices of housing attributes is violated; the repeat-sales estimator is extremely sensitive to influential observations; and the usual method used to correct for heteroskedasticity in repeat-sale housing returns is inappropriate in our sample. Hedonic techniques are better suited to contend with index number problems per se, as they can accommodate changing attribute prices over time. They also appear to give rise to more reliable estimates of price indices, as unusual observations have less effect on estimated price indices. Drawbacks of the hedonic approach include the usual concern with omitted attributes, and their effect on the estimated price index.     

Sample Selection Bias and Repeat-Sales Index Estimates

Analysis of variations in house values among localities requires reliable house value indices. Gatzlaff and Haurin (1994) indicate that traditional hedonic house value index estimates, using only information from a sample of sold homes to estimate value movements for the entire housing stock, may be subject to substantial bias. This article extends previous work by adapting the censored sample procedure to the repeat-sales index estimation model. Using data from Dade County, Florida, a house value index constructed from a sample of homes selling more than once, rather than all houses in a locality, is found to be biased. The bias is shown to be highly correlated with changes in economic conditions.     

Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks

We analyze the ability of an index of mortgage default risks (MDRI) for 43 states and 20 metropolitan statistical areas (MSA) of the US derived from Google search queries, in predicting (in- and out-of-sample) housing returns of the corresponding states and MSAs, based on various panel data and time-series approaches. In general, our results tend to prefer the panel data model based on common correlated effects estimation. We highlight that growth in MDRI negatively impacts housing returns within-sample, with predictive gains primarily concentrated beyond a year. These results are robust to alternative out-of-sample periods and econometric frameworks. Given the role of house prices as a leading indicators, our results are of value to policymakers, especially at the longer-run.     

EXCESS RETURNS OF INDEX REPLACEMENT STOCKS: EVIDENCE OF LIQUIDITY AND SUBSTITUTABILITY

Excess returns of S&P index replacement stocks are attributed to price pressures and imperfect substitutes in previous research. However, parameter estimates are biased by the use of pre-announcement returns; replacements are characterized by rising stock prices. Using a future estimation period to avoid this bias, we find excess returns do not reverse. Further, we find no relation between excess returns and the amount of stock closely held or the size of index funds. The evidence supports efficient market assumptions: the stock market is liquid and stocks are close substitutes.     

Removing biases in computed returns

This paper presents a straightforward method for asymptotically removing the well-known upward bias in observed returns of equally-weighted portfolios. Our method removes all of the bias due to any random transient errors such as bid-ask bounce and allows for the estimation of short horizon returns. We apply our method to the CRSP equally-weighted monthly return indexes for the NYSE, Amex, and NASDAQ and show that the bias is cumulative. In particular, a NASDAQ index (with a base of 100 in 1973) grows to the level of 17,975 by 2006, but nearly half of the increase is due to cumulative bias. We also conduct a simulation in which we simulate true prices and set spreads according to a discrete pricing grid. True prices are then not necessarily at the midpoint of the spread. In the simulation we compare our method to calculating returns based on observed closing quote midpoints and find that the returns from our method are statistically indistinguishable from the (simulated) true returns. While the mid-quote method results in an improvement over using closing transaction prices, it still results in a statistically significant amount of upward bias. We demonstrate that applying our methodology results in a reversal of the relative performance of NASDAQ stocks versus NYSE stocks over a 25 year window.     

Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.     

Time varying betas and the unconditional distribution of asset returns

This paper draws attention to the fact that under standard assumptions the time varying betas model cannot capture the dynamics in beta. Conversely, evidence of time variation in beta using this model is equivalent to non-normality in the unconditional distribution of asset returns. Using the multivariate normal as a model for the joint distribution of returns on market indices and predetermined information variables, it is shown how to capture skewness and kurtosis in the unconditional distributions of asset returns. Under the assumptions of the model, asset returns are unconditionally distributed as an extended quadratic form (EQF) in normal variables. Expressions are given for the moment generating function and for the computation of the distribution and density functions. The market-timing model is derived formally using this model. The properties of bias when the standard linear betas model is used to estimate alpha when the correct model is the EQF are also investigated. It is shown that a different time varying betas model can arise as a consequence of portfolio selection. It is also shown that the predetermined information variables have the potential to account for the time series properties of returns, including heterogeneity of variance. An empirical study applies the model to returns on 46 UK bond funds. An analysis of the residuals shows that the model described in this paper is able to capture the dynamics of alpha and beta and properly account for other features of the time series of returns for 28 of these funds, of which 15 exhibit time variation in beta. The study reports the effect of the EQF model on the computation of VaR and CVaR and bias in the estimation of alpha.     

Housing “Beta”: Common Risk Factor in Returns of Stocks

This study proposes the housing “beta” and tests whether the housing “beta” is a significant determinant for stock returns in a multifactor framework. We hypothesize that the housing market is a systematic risk factor given the impact of the housing market on the overall economy and economic growth of most countries, as well as the effect of homes in the overall wealth of individual investors. The housing market directly affect GDP growth through residential fixed investment and housing services. In addition, the housing market indirectly impacts economic activities via consumption. Our results show that the housing “beta” is positive and significant in explaining stock returns after controlling several other factors from the prior literature. This relationship is stronger, as expected, during the financial crisis period. We conducted several robustness checks using a different study period and housing market indices and obtain results which are consistent with our main findings.

     

Do fear indices help predict stock returns?

This study investigates the forecasting power of implied volatility indices on forward looking returns. Prior studies document that negative innovations to returns are associated with increasing implied volatility of the underlying indices; thus, suggesting a possible relationship between extremely high levels of implied volatility and positive short term returns. We investigate this issue by examining the predictive power of three implied volatility indices, VIX, VXN and VDAX, on the underlying index returns. We extend previous research by also focusing on characterised selected stocks and examine the relationship between implied volatility indices and future returns across different sectors and classified portfolios. Our findings suggest that implied volatility indices are good predictors of 20-days and 60-days forward looking returns and illustrate insignificant predictive power for very short term (1-day and 5-days) returns.     

The hedging effect of green bonds on carbon market risk

This paper explores effective hedging instruments for carbon market risk. Examining the relationship between the carbon futures returns and the returns of four major market indices, i.e., the VIX index, the commodity index, the energy index and the green bond index, we find that the connectedness between the carbon futures returns and the green bond index returns is the highest and this connectedness is extremely pronounced during the market's volatile period. Further, we develop and evaluate hedging strategies based on three dynamic hedge ratio models (DCC-APGARCH, DCC-T-GARCH, and DCC-GJR-GARCH models) and the constant hedge ratio model (OLS model). Empirical results show that among the four market indices the green bond index is the best hedge for carbon futures and performs well even in the crisis period. The paper also provides evidence that the dynamic hedge ratio models are superior to the OLS model in the volatile period as more sophisticated models can capture the dynamic correlation and volatility spillover between the carbon futures and market index returns.     

MEASURING LIMITS OF ARBITRAGE IN FIXED‐INCOME MARKETS

An emerging literature relies on an index of limits of arbitrage in fixed‐income markets. We analyze the benefits of an index that is model‐free, robust, and intuitive. This new index strengthens the evidence that limits of arbitrage proxy for risks priced in the cross‐section of returns. Trading simulations show that the new index improves identification of limits of arbitrage because it bypasses a noisy estimation step. Relative value indices in the United States, United Kingdom, Japan, Germany, Italy, France, Switzerland, and Canada exhibit strong commonality and high correlations with local volatility and funding conditions. The indices are updated regularly and available publicly.     

Inferring Price Information from Mortgage Payment Behavior: a Latent Index Approach

Existing price indices are based on real estate sales. This approach encounters problems when (1) sales are infrequent or (2) when these differ systematically from the overall market (selection bias). Relative to the number of properties sold on the market, a much greater number of properties have borrowers who need to make monthly mortgage payment decisions. Therefore, each month borrowers cast a vote of confidence or no confidence in their price relative to the loan balance. Based on this behavior, we invert the relation between mortgage performance and prices to derive a latent price index. Using a large sample of individual mortgages across the 10 cities investigated, the latent index in each city has a high correlation with the respective Case-Shiller index. In addition, the latent index is partially explained by the housing expectations (derived from futures on the respective Case-Shiller index) which indicates that it is not a purely reactive measure. Overall the results show that the latent index has potential to boost information resources for tracking the important real estate sector.     

A Spatial Model of Housing Returns and Neighborhood Substitutability

This article provides a method for estimating housing indices at the local level. It develops a distance-weighted repeat-sales procedure to exploit the factor structure of the error-covariance matrix in the repeat-sales model. A distance function defined in characteristic and geographical space provides weights for the generalized least-squares model, and allows the use of all of the repeated sales in a metropolitan area to measure returns for the specific neighborhood of interest. We use distance-weighted repeat sales to estimate return indices for all zip codes in the San Francisco Bay area over the period 1980--1994.When distance is defined in terms of socioeconomic characteristics, we find that median household income is the salient variable explaining covariance of neighborhood housing returns. Racial composition and educational attainment, while significant, are much less influential. Zip-code level indices often deviate dramatically from the citywide index, depending upon income levels. This has implications for investors and lenders. Our results indicate that rates of return may vary considerably within a metropolitan area. Thus, simply using broad metropolitan area indices as a proxy for capital appreciation within a specific neighborhood may not be justified.     

Price Indices Based on the Hedonic Repeat-Sales Method: Application to the Housing Market

Shiller (1993) proposes the hedonic repeated-measures (HRM) approach to measuring constant quality price indices for heterogeneous assets such as some bonds and real estate. We derive a mathematical relationship between the coefficients of the HRM model and those from the standard repeat-sales model, and we demonstrate how hedonic characteristics should be chosen for inclusion in the HRM model. Empirical estimates using Fairfax, Virginia, housing transactions data show that the HRM price index evaluated at the mean of the hedonic variable is virtually identical to the standard repeat sales index, just as predicted by our mathematical relationship. But the HRM allows estimation of different price paths for heterogeneous assets. We demonstrate that use of assessed value as the only hedonic characteristic allows parsimonious HRM estimates.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.     

Anyone for Tennis (Betting)?

Abstract

The most robust anomaly noted in the literature on wagering markets is (positive) longshot bias: over a period of 50 years, it has been well documented in horse betting that higher expected returns accrue to short- than to long-odds bets. However, a few examples of betting markets with zero or negative bias have been found, for example in certain American sports. The understanding of longshot bias is likely to be informed by comparing and contrasting conditions in markets displaying positive, zero, and negative bias but, to date, relatively few markets have been examined. This paper employs a large data set on professional men's tennis matches and a new econometric approach to the estimation of the relationship between returns and odds. It finds positive bias throughout the range of odds. It discusses this finding in the context of the debate on why biases exist and persist in wagering markets, focusing particularly on bettors’ attitudes towards risk and skewness.     

Liquidity costs,idiosyncratic volatility and expected stock returns

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-based returns and then estimate IV. We show that there is a premium for IV in value-weighted portfolios, but this premium is less strong after correcting returns for microstructure bias. We further show that this premium is driven by liquidity in the prior month after correcting returns for microstructure noise. The pricing results from equally-weighted portfolios indicate that IV does not predict returns either before or after controlling for liquidity costs. These findings are robust after controlling for common risk factors as well as analysing double-sorted portfolios based on IV and liquidity.     

What Drives Housing Markets: Fundamentals or Bubbles?

This study applies the dynamic Gordon growth model which is in the circumstance of rational bubbles to decompose log price-rent ratio into three parts, i.e., rational bubbles, discounted expected future rent growth rates and discounted expected future returns. The latter two terms represent housing fundamentals. The magnitudes of the components of price-rent ratio’s variance are estimated to distinguish the relative impact of the three parts on housing prices. Using time series data from the housing markets in the four largest cities in China (1991:Q1–2011:Q1 for Shanghai, Guangzhou and Shenzhen; 1993:Q2–2011:Q1 for Beijing), this paper presents a number of empirical findings: (a) the variance of rational bubbles is much larger than the variance of price-rent ratio, and rational bubbles contribute more fluctuations directly to price-rent ratio than the expected returns or the expected rent growth rates do; (b) the covariance between rational bubbles and expected returns or expected rent growth rates is also large; (c) the positive covariance of rational bubbles and expected returns implies that high expected returns coexist with bubbles, which differs from previous findings that lower expected returns drive asset prices; (d) the negative covariance of rational bubbles and expected rent growth rates indicates that the larger the bubbles are, the lower the expected rent growth rates are; (e) the positive covariance of expected returns and expected rent growth rates reveals under-reaction of the housing markets to rents.     

Volatility dynamics for the S&P 500: Further evidence from non-affine,multi-factor jump diffusions

We apply Markov chain Monte Carlo methods to time series data on S&P 500 index returns, and to its option prices via a term structure of VIX indices, to estimate 18 different affine and non-affine stochastic volatility models with one or two variance factors, and where jumps are allowed in both the price and the instantaneous volatility. The in-sample fit to the VIX term structure shows that the second (stochastic long-term volatility) factor is required to fit the VIX term structure. Out-of-sample tests on the fit to individual option prices, as well as in-sample tests, show that the inclusion of jumps is less important than allowing for non-affine dynamics. The estimation and testing periods together cover more than 21 years of daily data.