Frequency of Transaction and House Price Modeling

This article examines the characteristics and price behavior of repeatedly transacted properties. Using data from four U.S. counties, we estimate hedonic price models of properties grouped by transaction frequency, and compare estimated standard deviations and estimated appreciation rates by group.For each of four counties studied, we find that estimated house price appreciation is systematically higher among properties that transact more frequently. One possible explanation for this result is that purchasers make property improvements that are not adequately reflected in the available data.We also find that estimated standard deviations of the disturbance term show a marked decrease as the frequency of transaction increases. Since frequently transacting properties are not found to be systematically more homogeneous than seldomly transacting properties, we do not attribute this to any increase in homogeneity for frequently transacting properties, but rather to the length of time elapsed between transactions of properties.The findings of this article suggest that repeat-sales price models may need to be adjusted to account for cross-sectional variation in transaction probabilities---that is, the selectivity of the subsample of properties that transacted (or transacted repeatedly) during any finite study period.     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Neighborhood Diversity and House-Price Appreciation

This study examines changes in house prices relative to the level of and change in percent racial/ethnic composition for certain counties in Tampa and Orlando, Florida. Repeat-sales transactions between 1971 and 1997 are used to create a constant quality price index for each city. The index for Tampa shows that the average annual house price appreciation was 5.89 percent over the period 1970 through 1997. The index for Orlando shows that the average annual house price appreciation was 5.25 percent over the 1970 through 1997 period. When the Tampa index model is expanded to account for race/ethnicity, household factors, and economic factors, the level of African American population has no significant effect on house-price appreciation; however, the change in percent African American has a negative effect. The level of percent Hispanic population has a positive effect, and the change in percent Hispanic has a positive effect. The expanded Orlando model shows that the level of percent African American population has no significant effect on price appreciation, while the change in percent African American has a negative effect. The level of Hispanic population has a positive effect, while the change in percent in Hispanic has a negative effect.     

Controlling for Quality in House Price Indices

Given the importance of house prices it is not surprising that house price indices are used for many purposes. One of the factors that differentiates these indices is the house price determinants (such as structural characteristics and neighborhood quality) that are accounted for—that is, held constant. Indices are usually generated from house price regressions. It is shown that, regardless of the desired level of accounting, it is necessary to control for all significant determinants of house prices in these regressions to obtain unbiased estimates of the growth in house prices. An empirical example shows that not controlling for neighborhood quality can lead to substantial biases in estimates of house price appreciation rates even if the index does not account for this factor.     

Transaction Frequency and Commercial Property

Metrics using repeat sale data assume that frequently and infrequently sold properties are similar in capital expenditures, maintenance and other characteristics. Value-added investors concentrate on repositioning properties which requires capital investment and managerial skills. Returns using repeat sales likely overstate appreciation by misattributing this investment. Present results show that frequently and infrequently traded properties represent different property populations. The first sale of a repeat transaction sells at a significant discount compared to single sale properties while the second sale transacts at a premium. The results suggest that repeat sale indices may overstate price appreciation and represent returns for a different, relatively small cohort of properties when compared to the large number of properties that transact only once during a specific time period.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

The Impact of Range Pricing on Marketing Time and Transaction Price: A Better Mousetrap for the Existing Home Market?

In various markets around the country, some real estate professionals are employing a new pricing strategy that involves marketing homes for sale with a price range rather than a single asking price. This strategy is often touted as a mechanism that will attract more potential buyers to look at a house and thus result in reduced marketing times for existing homes, with prices determined by competitive forces. The purpose of this study is to empirically examine whether houses using range pricing, often referred to as value range marketing, sell in the same amount of time and sell for similar prices as those marketed in the traditional manner. Two staged least squares with a correction for sample selection and Weibull duration models are used to test the hypotheses, employing a sample of 5,852 residential houses that were sold during the period January 1999 to December 2000. In contrast to claims of the strategy’s proponents, the results indicate that houses take longer to sell when using the range pricing strategy after controlling for physical characteristics and market conditions. Furthermore, there is no evidence that this strategy has any significant impact on transaction prices.     

The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data

Using millions of individual gasoline prices collected at a daily frequency, we examine the speed at which market prices of refined oil are transmitted to retail gasoline prices in France. For that, we estimate a reduced‐form model of state‐dependent pricing where thresholds triggering price changes are allowed to vary over time and depend on the duration since the last price change. We find that the degree of pass‐through of wholesale prices to retail gasoline prices is on average 0.77 for diesel and 0.67 for petrol and depend on local market characteristics. The duration for a shock to be fully transmitted into prices is about 10 days. There is no significant asymmetry in the transmission of wholesale price to retail prices. Finally, the duration since the last price change has a significant effect on thresholds triggering price changes but a large variance of idiosyncratic shocks on thresholds is also crucial to replicate the size distribution of price changes.     

Analysis of Spatial Autocorrelation in House Prices

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.     

A New Spin on the Jumbo/Conforming Loan Rate Differential

This article uses house-price transaction data to estimate volatility in house prices. The volatility parameter is an input into a mortgage-pricing model that is used to simulate the contract interest rate that balances the mortgage contract. By segmenting the house-price transaction into high- and low-valued homes, we are able to estimate a theoretical jumbo/conforming loan rate differential. Simulation results demonstrate that the differences in volatility between high- and low-priced homes can produce a contract loan rate differential, holding all else constant. The article also presents a discussion of the problems inherent to estimating volatilities form assets with infrequent trades and long holding periods.     

Capital inflows and asset prices: Evidence from emerging Asia

The withdrawal of foreign capital from emerging countries at the height of the recent financial crisis and its quick return sparked a debate about the impact of capital flow surges on asset markets. This paper addresses the response of property prices to an inflow of foreign capital. For that purpose we estimate a panel VAR on a set of Asian emerging market economies, for which the waves of inflows were particularly pronounced, and identify capital inflow shocks based on sign restrictions. Our results suggest that capital inflow shocks have a significant effect on the appreciation of house prices and equity prices. Capital inflow shocks account for – roughly – twice the portion of overall house price changes they explain in OECD countries. We also address cross-country differences in the house price responses to shocks, which are most likely due to differences in the monetary policy response to capital inflows.     

Spatial Variation in Reverse Mortgages Usage: House Price Dynamics and Consumer Selection

Reverse mortgages have been obtained by nearly one million senior households. In the future, the number of eligible households will grow substantially, about 80 % are homeowners, and many of them have substantial equity in their home. We study state-level variations in rate of originations of HUD’s Home Equity Conversion Mortgage (HECM) product. Our focus is on the impact of house prices on the origination rate. We test the hypothesis that in states where real house prices are volatile and the current level is above the long term norm, seniors rationally anticipate future reductions in house prices and lock-in their housing equity gains by obtaining a reverse mortgage. We test alternative hypotheses, the first being that seniors living in states with high rates of house price appreciation increase their use of HECMs as a means to convert an illiquid wealth capital gain into a more liquid asset. A second alternative hypothesis is that the intertemporal changes in originations of HECMs were a result of changes in the supply of mortgage originators. Our empirical work supports the hypothesis that seniors used HECMs to insure against house price declines, but we find no evidence in support of the alternative hypotheses.     

The effect of real rates of interest on housing prices

During the late 1970s, U.S. house prices were appreciating rapidly even though mortgage interest rates were climbing. Recently, interest rates have eased but prices have moderated. This study examines the role of appreciation expectations in overcoming the negative effects of nominal mortgage interest rates on house prices. Expectations of future appreciation are important determinants of house sales prices, remaining influential during periods of declining and moderating real prices, not just when prices are rising. The real rate of interest, as viewed by the homebuyer, is the mechanism for affecting change in housing price levels. Because the nominal interest rate is slow to reflect changes in expectations, these real rates vary over time. This ebb and flow of real interest rates appears to explain market price levels. Nominal rates play a role as well, primarily in the formation of appreciation expectations.     

Appraisals,Transaction Incentives,and Smoothing

This article is structured around three principal objectives. The first is to determine whether any incentives for appraisals support an underlying purchase offer, which may be termed a transaction bias. Appraisals that are lower than purchase prices could involve additional cost for justification and thus undermine the transaction. The second objective is to test whether appraisal data are smoothed or exhibit less volatility than purchase data. The article compares the volatility of separate appraisal and purchase data. Given separate appraisal and purchase time series, the third objective is to derive the implied optimal appraisal updating rule.The model is applied to appraisal and purchase price indices for 3.7 million repeat transactions on mortgages bought by Fannie Mae and Freddie Mac by using monthly data from January 1975 to December 1993. The estimation procedure uses generalized autoregressive conditioned heteroskedastic (GARCH) analysis to take account of persistence in means and volatility in the house price time series. The article draws three principal conclusions. First, appraisals are systematically higher than purchase data, a first-moment differential. Second, appraisal smoothing does not occur generally. Third, the appraisal updating rule for the United States appears to involve error correction whereby underappraisals from pervious periods are eventually adjusted.     

After-Hours Stock Prices and Post-Crash Hangovers

After-hours pricing in foreign equity markets of multiple-listed U.S. securities appeared to be efficient in predicting New York prices in the weeks immediately following the October 1987 crash but relatively uninformative in succeeding months. By contrast, daily changes in New York prices appear to be efficiently incorporated in after-hours trading on both the Tokyo and London exchanges throughout the sample period. This paper suggests that the asymmetry and temporal variations in cross-market correlations are consistent with rational investor behavior in equity markets with nonzero transaction costs and time-varying share price volatility.     

Should we Fear the Shadow? House Prices,Shadow Inventory,and the Nascent Housing Recovery

Although a broad-based increase in house prices has been observed over the past year, not everyone is convinced the rise of house prices will persist and lead to a steady recovery of the economy. The main reason for this skepticism is uncertainty about the “shadow inventory”: foreclosed homes held by investors or as REOs, which have not yet hit the market but likely will as market prices rise. The volume of shadow inventory itself in local markets is largely unknown, as is its impact on the housing market. This study quantifies the size of the shadow inventory and investigates the spatial impact of the out-flow of shadow inventory. The scope of our study is a set of housing markets (AZ, CA, and FL) that vary in both their historic housing price volatility as well as institutional factors - such as foreclosure law statutes - that may influence the relationship between the shadow inventory and house price dynamics. To address the endogeneity that characterizes the spatial interaction of house prices and the out-flow of the shadow inventory, we utilize a simultaneous equation system of spatial autoregressions (SESSAR). The model is estimated using measures of the shadow inventory derived from DataQuick’s national transaction history database and county-level house price indices provided by Black Knight. Lastly, because our estimate - as well as all other existing estimates - of the shadow inventory relies upon string matching algorithms to identify entry into and exit out of REO status, we validate the accuracy of our measures of REOs using loss mitigation data from the OCC Mortgage Metrics database.     

Do Capital Flows Matter to Stock and House Prices? Evidence from China

This article analyzes the impacts of foreign direct investment (FDI) and short-term capital flows, otherwise known as hot money, on stock and house prices in China. Empirical results, estimated using the local projections approach, reveal that a positive hot money net inflow shock significantly increases stock and house prices and the impacts persist for up to 1–2 months, while a positive FDI net inflow shock contributes significantly to lagged house price appreciation but has no effect on stock prices. This study also identifies negative pass-through effects of FDI net inflows on hot money net inflows and positive pass-through effects of stock prices on house prices.     

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.     

Fair Value: Your Value or Mine? An Observation on the Ambiguity of the Fair Value Notion Illustrated by the Credit Crunch

Current International Financial Reporting Standards (IFRSs) define fair value as a transaction price. In imperfect markets, buyer's and seller's marginal prices, at which they are rationally willing to transact, differ. The transaction price can be any amount within the range between those prices. However, scenarios are conceivable in which no such range exists because the seller's marginal price exceeds the buyer's. In this scenario, no arm's length transactions between knowledgeable, willing parties are possible. Such a scenario can be likely characterised by low liquidity and/or high information asymmetry and seems to be broadly consistent with what is recently referred to as the ‘credit crunch’. Under this scenario, the IFRS definition of fair value is not readily applicable. Two views are possible: under view 1, fair value refers to the potential buyer's marginal price. Although fair value does always exist conceptually, it negates the notion of two rationally acting parties. View 2 acknowledges that no arm's length transaction is possible, resulting in the fair value notion not being applicable. If these two views are applied to the IFRS definition of an active market, view 1 results in markets that are always active. Only view 2 allows distinguishing between active and inactive markets.     

Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

This paper examines the role of nonfundamentals‐based sentiment in house price dynamics, including the well‐documented volatility and persistence of house prices during booms and busts. To measure and isolate sentiment's effect, we employ survey‐based indicators that proxy for the sentiment of three major agents in housing markets: home buyers (demand side), home builders (supply side), and lenders (credit suppliers). After orthogonalizing each sentiment measure against a broad set of fundamental variables, we find strong and consistent evidence that the changing sentiment of all three sets of market participants predicts house price appreciation in subsequent quarters, above and beyond the impact of changes in lagged price changes, fundamentals, and market liquidity. More specifically, a one‐standard‐deviation shock to market sentiment is associated with a 32–57 basis point increase in real house price appreciation over the next two quarters. These price effects are large relative to the average real price appreciation of 71 basis points per quarter observed over the full sample period. Moreover, housing market sentiment and its effect on real house prices is highly persistent. The results also reveal that the dynamic relation between sentiment and house prices can create feedback effects that contribute to the persistence typically observed in house price movements during boom and bust cycles.