FHA Mortgage Discount Points, House Prices and Consumer Behavior

The allocation of FHA discount points between buyers and sellers has recently been the subject of empirical investigation. The research reported here examines this relationship in more detail. Regression equations were estimated for separate price categories of housing using dummy variables for the number of discount points being charged. Separate equations were also estimated for periods when points were rising and when they were falling. The results essentially are that sellers attempt to shift all of the discount points to buyers and frequently are successful. However, in certain situations the points are shared between buyers and seller.     

Optimal Mortgage Refinancing with Stochastic Interest Rates

The purpose of this paper is to develop a dynamic model of mortgage refinancing in a contingent claim framework that simultaneously solves for the borrower's optimal mortgage refinancing strategy, the value of the refinancing call option, the value of the mortgage liability to the borrower, and the market (lender) value of the fixed-rate contract. We also calculate the minimum differential between the contract rate on the existing mortgage and the current interest rate that is required to trigger an optimal mortgage refinancing.     

House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.     

Determinants of GNMA Mortgage Prices

This paper contrasts three different arbitrage-based models for the pricing of GNMA securities, and analyzes the effect of different assumptions about the call policy pursued by the issuers of the underlying mortgages. Both the nature of the interest-rate uncertainty captured by the model and the assumed call policy have a major effect on the yield differentials predicted between GNMA securities and Treasury Bonds.     

Border Effects in House Prices

This article estimates the effect of the Dutch–German border on house prices. We argue that the difference between house prices at the border indicates the willingness to pay to stay in a country compared to living across the border. After a change in the tax rules in 2001, migration from the Netherlands to Germany increased substantially and the gradient of Dutch house price towards the German border steepened. Combining a German and Dutch real estate dataset and using different estimation strategies, we find that asking prices of comparable housing drop by about 16% when one crosses the Dutch–German border.     

The Effects of Charm Listing Prices on House Transaction Prices

As is the case for many different goods and services, it is common practice in many real estate markets for sellers to offer properties for sale at listing prices just below some round number price ( e.g. , $99,900 instead of $100,000). The academic marketing literature refers to this practice as "charm" pricing and suggests that this strategy is an attempt by sellers to take advantage of buyers' cognitive processes in which charm prices affect buyers' perceptions about the seller or the item being offered for sale. Although numerous papers in the housing economics literature have addressed the impact of the magnitude of listing price on observed house transaction prices, no prior published study has considered the impact of the design of listing prices in housing markets. This paper presents an empirical investigation of the effects of charm pricing on house transaction prices using sample data. The results provide some evidence that houses listed at certain charm prices sell for significantly greater transaction prices than those listed at round number prices.     

An Empirical Test of a Two‐Factor Mortgage Valuation Model: How Much Do House Prices Matter?

This article develops a two-factor structural mortgage pricing model in which rational mortgage-holders choose when to prepay and default in response to changes in both interest rates and house prices. We estimate the model using comprehensive data on the pool-level termination rates for Freddie Mac Participation Certificates issued between 1991 and 2002. The model exhibits a statistically and economically significant improvement over the nested one-factor (interest-rate only) model in its ability to match historical prepayment data. Moreover, the two-factor model produces origination prices that are significantly closer to those quoted in the to-be-announced market than the one-factor model. Our results have important implications for hedging mortgage-backed securities.     

The Measurement and Determinants of Single-Family House Prices

We assess the conceptual and empirical features of a number of house price series for the United States. We then calculate a measure of the net up-grading of the existing stock of houses that took place during the 1950–1989 period and adjust price indexes for this net increase in quality. Judgments about the trend, volatility, and determinants of house prices are shown to depend crucially on which price series is used. The Freddie Mac upgrade-adjusted house price measure rose 5.7% over the past four decades, falling 7.7% from 1950 through 1970 before rising 14.5% from 1970 through 1989. Real house prices declined in the early 1980s due to the increase in real after-tax interest rates and the decline in real materials costs. The recovery of house prices in the late 1980s is attributed to lower unemployment and real after-tax interest rates and particularly to demographic factors associated with the aging of baby boomers.     

Government Policies,Residential Mortgage Defaults and the Boom and Bust Cycle of Housing Prices

We develop a micro‐based macromodel for residential home prices in an economy where defaults on residential mortgages negatively affect housing prices. Our model enables us to study the impact of subprime defaults on prime borrowers and the impact of various government policies on the housing market boom and bust cycle. In this regard, our key conclusions are that (i) there is a contagion effect from subprime defaults to prime defaults due to the negative impact of subprime defaults and (ii) monetary policy is the most effective tool for decreasing mortgage defaults and increasing aggregate home prices in contrast to alternative government fiscal policies designed to loosen mortgage credit.     

Testing for Cointegration between House Prices and Economic Fundamentals

Economic theory predicts three possibilities for the cointegration relationship between house prices and economic fundamentals: linear cointegration, nonlinear cointegration and no cointegration. In contrast, the empirical literature has only examined linear cointegration. This article argues that ignoring nonlinear cointegration may lead to misleading conclusions that no cointegration exists between house prices and the fundamentals. To illustrate this point, I test for cointegration for ten U.S. cities and find that only one city shows evidence of linear cointegration. Further analysis using the two‐step testing procedure yields evidence of nonlinear cointegration for six other cities. Still, there are three cities left out without evidence of nonlinear cointegration. Further studies are needed to test for other forms of nonlinear cointegration before a conclusion of no cointegration can be reached for the remaining three cities.     

The Effect of Mortgage Rate Buydowns on Housing Prices:

The model developed in this paper analyzes the effect of builder-financed FHA-VA mortgage subsidies or buydowns on the price of housing. Hedonic pricing equations are estimated for a locationally and qualitatively uniform sample of new tract development homes. The explanatory variables are vectors of physical and financial characteristics. The latter include a continuous variable for discount points paid by builders which is indicative of the magnitude of prepaid finance charges. The results indicate that a substantial portion of mortgage subsidy costs are shifted to buyers in the form of inflated housing prices.     

U.S. Monetary Policy Surprises and Mortgage Rates

This article examines how the U.S. monetary policy surprises impact the mortgage rates in the nation and across five regions from 1990 to 2008. Regression analysis based on bootstrapping shows that surprises in the target federal funds rate (the target factor) have a significantly positive impact on the 1‐year adjustable‐rate mortgage (ARM) rate within the week of the Federal Open Market Committee announcements and the positive impact lasts up to 1 week after the announcements. Surprises in the future direction of the Federal Reserve monetary policy (the path factor) have significantly positive impacts on both the 1‐year ARM rate and the 30‐year fixed mortgage rates in the first week after the announcement. Furthermore, the responses of mortgage rates are asymmetric and affected by the size of monetary policy surprises, the stage of the business cycle and whether the monetary policy is tightening or loosening. There also exists heterogeneity in the mortgage rate pass‐through process across regions and monetary policy surprises have differential impacts on the regional mortgage rates. The cross‐region variations are mainly correlated with the regional housing market conditions, such as home vacancy and rental vacancy rates.     

The Effect of Housing Government-Sponsored Enterprises on Mortgage Rates

We derive a theoretical model of how jumbo and conforming mortgage rates are determined and how the jumbo–conforming spread might arise. We show that mortgage rates reflect the cost of funding mortgages and that this cost of funding can drive a wedge between jumbo and conforming rates. Further, we show how the jumbo–conforming spread widens when mortgage demand is high or core deposits are not sufficient to fund mortgage demand, and tightens as the mortgage market becomes more liquid and realizes economies of scale. Using Mortgage Interest Rate Survey data for April 1997 through May 2003, we estimate that the government-sponsored enterprise funding advantage accounts for about 7 basis points of the 15–18 basis point jumbo–conforming spread.     

Do Deposit Rates Cause Mortgage Loan Rates?: The Evidence from Causality Tests

This paper applies two familiar causality detection techniques to the issue of whether it is costs that determine prices or vice versa in the mortgage loan market. The question is posed in terms of causal priority: Are savings and loan deposit rates causally prior to mortgage loan rates or is it the other way around? For the time period prior to the onset of deposit interest rate deregulation, the evidence that emerges is consistent with the view that lenders raised their loan rates in response to higher deposit rates of interest. However, for the more recent period of deregulation, the evidence is not consistent with this view.     

Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices

While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting.     

Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out–of–sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.     

The Long‐Run Relationship Between House Prices and Rents

I use standard error‐correction models and long‐horizon regression models to examine how well the rent–price ratio predicts future changes in real rents and prices. I find evidence that the rent–price ratio helps predict changes in real prices over 4‐year periods, but that the rent–price ratio has little predictive power for changes in real rents over the same period. I show that a long‐horizon regression approach can yield biased estimates of the degree of error correction if prices have a unit root but do not follow a random walk, and I construct bootstrap distributions to conduct appropriate inference in the presence of this bias. The results lend empirical support to the view that the rent–price ratio is an indicator of valuation in the housing market.     

On the Nature of Self‐Assessed House Prices

In models of optimal household behavior, the value of housing affects consumption, savings and other variables. But homeowners do not know the value of their house for certain until they sell, so while they live in their home they must rely on local house price data to estimate its value. This article uses data from the recent housing boom and bust to demonstrate that changes in households' self‐assessed home values are strongly consistent with the predictions of a model in which households optimally filter available house price data. Specifically, we show that self‐assessed house prices did not increase as rapidly as house price indexes during the boom and did not decline as severely during the bust. A Kalman filter model nearly perfectly replicates these data. These findings have direct implications for economists studying asking prices during booms and busts, optimal default decisions and other key housing‐related phenomena.     

A Note on Mortgage Risk: Default vs. Loss Rates

This note reexamines the role of the loan-to-value ratio on mortgage risk. Whereas previous studies have focused on the default rate as a function of this term, this study considers the additional effect on the loss rate of defaulted loans. Because the dollar loss per amount originated is the product of the default rate and the loss rate on defaulted loans, the impact of the loan-to-value ratio on both the default and loss rates is crucial to explaining the impact of the loan-to-value ratio on mortgage risk. I find that both rates are significantly positively related to loan-to-value ratio and that the loss rate accounts for between 13% and 20% of total loan-to-value impact.