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.     

House Prices and the Structure of Local Government: An Application of Spatial Statistics

When two internally homogeneous communities decide to jointly provide a public service, residents of each community lose some control over the public service provision. The loss of control over public schooling provision contributes to a $2,929 or 3.5 percent drop in constant-quality house value. Increased heterogeneity of the consolidated district is responsible for almost all the drop; the increased number of service recipients alone is responsible for almost none of the drop. The spatial hedonic, corrected for sample selection bias, also suggests economies of scale gains from school district consolidation must be worth at least $3,369—4 percent of house value.     

The impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area

Geography has previously been noted as a decisive factor in business literature. This paper provides evidence of the significant role geography plays in customer lapse behaviour in an urban environment. This novel approach is based on the idea that the customers who cancel all policies and leave the company are not randomly distributed; rather, a mimetic performance of close individuals is noted. The physical proximity of the customer to the geographical focus (strategical centre, as insurance offices) and the interaction with nearby customer are spatial factors that increase (or decrease) the probability of churning. An empirical analysis using more than 7000 spatially georeferenced offline customers of a Spanish insurance company in the urban area of Madrid (Spain) demonstrated that the customer's proximity to offices of such insurance company under study decreases the probability of churning, whereas high lapse risk was detected in customers in the surroundings of the company's competitor branches. In addition, we identified spatial autocorrelation in churn probability, thus demonstrating that the probability of churn of a customer increases if nearby customers churn.     

House Prices, Fundamentals and Bubbles

Abstract:  This paper studies actual (real) house prices relative to fundamental (real) house values. Such a focus is warranted since housing constitutes a large fraction of most household portfolios, and its characteristics are such that, in contrast to what prevails in financial markets, arbitrage will be limited and hence correction toward 'true' value is likely to be a prolonged process. Using UK data and a time-varying present value approach, our results preclude the existence of an explosive rational bubble due to non-fundamental factors. We further find that intrinsic bubbles have an important role to play in determining actual house prices although price dynamics appear to impact, particularly in periods of strong deviation from fundamental value. Price dynamics are found to be driven by momentum behaviour.     

House Prices and Government Spending Shocks

We show that dynamic stochastic general equilibrium (DSGE) models with housing and collateralized borrowing predict a fall in house prices following positive government spending shocks. By contrast, we show that house prices in the United States rise persistently after identified positive government spending shocks. We clarify that the incorrect house price response is due to a general property of DSGE models—approximately constant shadow value of housing—and that modifying preferences and production structure cannot help in obtaining the correct house price response. Properly accounting for the empirical evidence on government spending shocks and house prices using a DSGE model therefore remains a significant challenge.     

Joint Dynamics of House Prices and Foreclosures

This paper studies the joint transitional dynamics of the foreclosures and house prices in a standard life‐cycle incomplete markets model with housing and a realistic long‐term mortgage structure. We calibrate our model to match several long‐term features of the U.S. housing market, and analyze the effects of several unexpected and permanent shocks on the house price and the foreclosure rate both across the steady states and along the transition between the steady states. We examine permanent, unexpected shocks to the risk‐free interest rate, the minimum down‐payment ratio, and unemployment. During the transition, these shocks create large movements in house prices. More importantly, the foreclosure dynamics are quite significant along the transition compared to the steady‐state changes, and there are strong feedbacks between foreclosures and house prices. We assess the effects of a temporary reduction in the risk‐free interest rate, which has moderate effects on house prices but little effect on foreclosure dynamics. We also study the effects of an ex ante macroprudential policy, which establishes a minimum down‐payment requirement at a higher threshold. Such a macroprudential policy helps substantially stabilize both house prices and foreclosures.     

School Quality, House Prices and Liquidity

This paper develops an empirical framework for taking into account the effects of endogenous liquidity on price capitalization estimates. Changes in school attendance zones in the East Baton Rouge Parish public school district provide a natural experiment for studying how changes in school characteristics affect house prices and liquidity. House price and selling time, or liquidity, are simultaneously determined in search markets. The empirical model exploits variation in the surrounding neighborhood market conditions pertinent to each house to identify the system of price and liquidity equations. The estimates are consistent with search-market theory in that liquidity absorbs part of the capitalization of school quality.

House Prices and Bubbles in New Zealand

This paper studies actual (real) house prices relative to fundamental (real) house values in New Zealand for the period 1970–2005. Utilizing a dynamic present value model, we find disparities between actual and fundamental house prices in the early 1970s and 1980s and from 2000 to date. We model the bubble component that is related to fundamentals (the intrinsic component), making it possible to highlight whether a bubble still exists after that component is accounted for. We then analyze any remaining bubble to detect any momentum behavior. Much of the overvaluation of the housing market is found to be due to price dynamics rather than an overreaction to fundamentals.

Optimal Macroprudential Policies and House Prices in Korea

This article investigates the impacts of the macroprudential policy of limitation on credit growth in housing market on Korean economy to find empirical and theoretical implications. Empirical results based on VAR models show that macroprudential policies like LTV and DTI in Korea have significant and persistent effect on real household credit and real house price. This article further addresses implications of optimal macroprudential and monetary policy in Korea by employing a standard DSGE model. The results suggest that the time-varying macroprudential policy responding to the borrower’s debt to income ratio is most effective in stabilizing household debt among the macroprudential policy rules considered, but produces a moderate downturn of the economy.     

House Prices and Local Taxes in the UK

In this paper, estimates of the effects of local domestic property taxes (rates) on local house prices are presented, and the effect of local taxes on owner-occupied dwelling prices is calculated for a number of English cities for the period up to 1990. The methods used enable estimation to be made of the effect of the introduction, during 1990, of the Community Charge or poll tax in England, when the local tax base was moved from housing consumption onto individual residency. It is estimated that the reform could have increased house prices by around 15 per cent and contributed substantially to house price inflation.     

Determinants of House Prices: A Quantile Regression Approach

OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.

Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models

This research reports results from a competition on modeling spatial and temporal components of house prices. A large, well-documented database was prepared and made available to anyone wishing to join the competition. To prevent data snooping, out-of-sample observations were withheld; they were deposited with one individual who did not enter the competition, but had the responsibility of calculating out-of-sample statistics for results submitted by the others. The competition turned into a cooperative effort, resulting in enhancements to previous methods including: a localized version of Dubins kriging model, a kriging version of Clapps local regression model, and a local application of Cases earlier work on dividing a geographic housing market into districts. The results indicate the importance of nearest neighbor transactions for out-of-sample predictions: spatial trend analysis and census tract variables do not perform nearly as well as neighboring residuals.     

Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices

In this article different spatial statistics techniques to analyze the behavior of used dwelling market prices are compared. We fit two lattice models: simultaneous and conditional autoregressive, a geostatistical model, the so-called universal kriging and finally, a linear mixed-effect model. Different spatial neighborhood structures are considered, as well as different spatial weight matrices and covariance models. The results are illustrated through a real data set of 293 properties from Pamplona, Spain.     

Spatial Statistics and Real Estate

Real estate has historically employed statistical tools designed for independent observations while simultaneously noting the violation of these assumptions in the form of clustering of same sign residuals by neighborhood, along roads, and near facilities such as airports. Spatial statistics takes these dependencies into account to provide more realistic inference (OLS has biased standard errors), better prediction, and more efficient parameter estimation. This article provides an overview of the field and directs readers to the relevant literature and software.     

A State Space Model for Berlin House Prices: Estimation and Economic Interpretation

Hedonic regression has become the standard approach for modeling the behavior of house prices. Usually, the common price component is modeled via dummy variables. Based on an approximation for the present value, we deliver an economic interpretation of the common price component. This allows to include explanatory factors like inflation rates, mortgage rates and building permissions. The notional rents for houses are fitted with a flexible hedonic function. We use the EM algorithm to estimate our model with monthly data of single-family house sales from the four South-West districts of Berlin, Germany from 1982:8 to 1999:12. Emphasis is put on the interpretation of the results.     

Optimal Monetary Policy Rules and House Prices: The Role of Financial Frictions

We probe the scope for reacting to house prices in simple and implementable monetary policy rules, using a New Keynesian model with a housing sector and financial frictions on the household side. We show that the social‐welfare‐maximizing monetary policy rule features a reaction to house price variations, when the latter are generated by housing demand or financial shocks. The sign and size of the reaction crucially depend on the degree of financial frictions in the economy. When the share of constrained agents is relatively small, the optimal reaction is negative, implying that the central bank must move the policy rate in the opposite direction with respect to house prices. However, when the economy is characterized by a sufficiently high average loan‐to‐value ratio, then it becomes optimal to counter house price increases by raising the policy rate.     

The Evolution of Market Efficiency: 103 Years Daily Data of the Dow

Autocorrelation in daily returns of the Dow 30 Index fluctuates significantly over time and reveals a declining trend after World War II. The relation between autocorrelation and volatility is negative and nonlinear. The relation between autocorrelation and volume is also negative and nonlinear. Returns exhibit positive autocorrelation during years with higher autocorrelation, and negative autocorrelation during years with lower autocorrelation. Positive autocorrelation appears more frequently during periods of low volatility, while negative autocorrelation appears more frequently during periods of high volatility. Current period's autocorrelation is related to previous period's autocorrelation and to both the previous and the current period's volatility and rate of return, which implies that investors incorporate previous period's pattern of market behavior into their trading strategy.     

金融业集聚对城镇化的影响及空间效应研究

依据全国31个省、市、自治区的省域空间面板数据,构建空间面板模型,考量金融业集聚对城镇化的影响及空间外溢效应。结果显示：我国省域城镇化率大部分集中在HH象限（高值集聚）和LL象限（低值集聚）,呈现较为明显的空间自相关性;全国层面,金融业集聚对城镇化的直接效应系数和间接效应系数均显著为正,说明金融业集聚对本省城镇化有显著的正向影响,且具有正向的空间外溢效应,能促进周边省份的城镇化;分东、中、西区域层面,东部地区金融业集聚对城镇化具有正向的影响,且具正向空间外溢效应,但在中西部地区,影响均不显著。基于此,应加强金融业集聚区建设,构建金融业集聚推进城镇化作用机制,在东中西区域科学布局建设金融中心。