On nonparametric estimation of a hedonic price function

Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar (Journal of Applied Econometrics 2007; 22 : 695–699) found that a nonparametric approach for estimating a hedonic house price function is superior to formerly suggested parametric and semiparametric specifications. We carefully reanalyze these specifications for this dataset by applying a recent nonparametric specification test and simulation‐based prediction comparisons. For the case at issue our results suggest that a previously proposed parametric specification does not have to be rejected and we illustrate how nonparametric methods provide valuable insights during all modeling steps. Copyright © 2010 John Wiley & Sons, Ltd.     

城市住房价格指数编制方法的拓展设计及实证检验

科学监测城市房价走势,在当前环境下尤为重要。为拓展国际通行方法编制国内单一城市房价指数的适用性,引入样本匹配重复交易法构建房价指数,以提高样本容量与可比性。基于上海数据的实证结果表明,相较于传统重复交易法和特征价格法,样本匹配重复交易法能更准确地反映住房价格变动,结果异常波动性更小,噪声影响程度更低,在克服样本代表性误差和变量缺失误差方面效果更显著,对编制国内城市房价指数具有较好应用价值。     

HEDONIC PRICE INDEXES FOR RESIDENTIAL HOUSING: A SURVEY,EVALUATION AND TAXONOMY

Every house is different. It is important that house price indexes take account of these quality differences. Hedonic methods which express house prices as a function of a vector of characteristics (such as number of bedrooms and bathrooms, land area and location) are particularly useful for this purpose. I consider here some developments in the hedonic methodology, as it is applied in a housing context, that have occurred in the last three decades. A number of hedonic house price indexes are now available. However, it is often difficult to see how these indexes relate to each other. For this reason I attempt to impose some structure on the literature by developing a taxonomy of hedonic indexes, and then show how existing indexes fit into this taxonomy. Also discussed are some promising areas for future research in the hedonic field. In particular, greater use needs to be made of spatial econometric and nonparametric methods to exploit the increased availability of geospatial data. The main criticisms of the hedonic approach are evaluated and compared with those of the repeat‐sales and stratified median methods. The overall conclusion is that the advantages of the hedonic approach outweigh its disadvantages.     

A house price index based on the SPAR method

Within the European Union there has been a push to provide European governments and the European Central Bank with the statistics they need for monitoring the owner-occupied sector. This paper reports on the results of a project to develop a house price index for the Netherlands. From January 2008, Kadaster, the Dutch land registry office, and Statistics Netherlands began jointly publishing house price index numbers for the whole country and for some specific dwelling types and regions. A number of special institutional features of the situation in the Netherlands contributed to the choice of index construction method. The indexes are computed using the Sale Price Appraisal Ratio (SPAR) method, which utilizes the ratios of transaction prices and previous appraisal values. We describe the SPAR method, compare it with repeat sales methods and assess the reliability of the official Dutch appraisal values. Empirical results for January 1995–March 2009 are presented. The SPAR method performs well compared to repeat sales, and the results reported will be of interest to other countries that have, or could instigate, institutional arrangements similar to those in the Netherlands.     

Nonparametric estimation and testing of fixed effects panel data models

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics.     

Testing a linear regression model against nonparametric alternatives

Summary As a test statistic for testing goodness-of-fit of a linear regression model, we propose a ratio of quadratic forms measuring the distance between parametric and nonparametric fits, relative to the estimated error variance. The test statistic is a modification of the statistic suggested by H?rdle and Mammen (1988). The asymptotic distribution under the hypothesis is established. The finite sample behaviour of the test is investigated in a Monte Carlo study, and is illustrated for two applications.     

Issues in measuring a monthly house price index in New Zealand

The sale price appraisal ratio (SPAR) method, which takes the ratios of the current house prices and their previous assessed values to construct an index, has been applied in New Zealand since the 1960s. This paper uses housing market transaction data for 12 cities in New Zealand (1994–2004) to develop monthly SPAR house price indices. These indices were subjected to a variety of statistical tests and benchmarked against the comparable monthly quality controlled weighted repeat sales indices. Finally, the paper provides some useful suggestions for future research based on the SPAR index and other alternative house price indices, such as the assessed value (AV) method.     

Daily House Price Indices: Construction,Modeling, and Longer‐run Predictions

We construct daily house price indices for 10 major US metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat‐sales method that closely mimics the methodology of the popular monthly Case–Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of longer‐run monthly house price changes that are superior to various alternative forecast procedures based on lower‐frequency data. Copyright © 2015 John Wiley & Sons, Ltd.     

The Valuation of Real Capital: A Random Walk down Kungsgatan

This paper analyzes the temporal pattern of prices for single-family housing. We estimate models of house price dynamics using a repeat sales framework, and we use the results to test for a random walk in asset prices. For eight large samples of housing transactions, representing essentially all house sales in Sweden during a 12-year period, we reject the hypothesis that house prices follow a random walk in favor of a model of first-order serial correlation.     

Variable selection for additive partially linear models with measurement error

Variable selection for additive partially linear models with measurement error is considered. By the backfitting technique, we first propose a variable selection procedure for the parametric components based on the smoothly clipped absolute deviation (SCAD) penalization, and one-step spare estimates for parametric components are also presented. The resulting estimates perform asymptotic normality as well as an oracle property. Then, two-stage backfitting estimators are also presented for the nonparametric components by using the local linear method, and the structures of asymptotic biases and covariances of the proposed estimators are the same as those in partially linear model with measurement error. The finite sample performance of the proposed procedures is illustrated by simulation studies.     

Revealed preference for relative status: Evidence from the housing market

This paper investigates the value individuals place on their relative housing consumption as compared to absolute housing consumption. Using observed housing sales from three Ohio MSAs in 2000, a spatial Durbin hedonic price model provides total marginal willingness-to-pay estimates for both characteristics of housing units and those of its neighbors. Using this revealed-preference approach, we find evidence suggesting individuals do value relative house size, but the absolute effect dominates. For instance, the estimates indicate that if all homes in Columbus were to increase in size by 100 square feet, the net effect of impacts on absolute and relative consumption would be to increase house prices by $605 on average. This stands in contrast to the stated preference literature, which frequently find individuals to be willing to forgo absolute well-being in exchange for relative status gains.     

The Accuracy of Price-Earnings and Discounted Cash Flow Methods of IPO Equity Valuation

This paper compares estimates of value derived from conventional discounted cash flow and price earnings valuation methods to the market price. For a sample of 45 firms newly listed on the New Zealand Stock Exchange our results suggest that the best discounted cash flow method and the best price earnings comparable have similar accuracy. The median absolute pricing error is around 20% and the models explain around 70% of the cross-sectional variation in market price scaled by book value. The results serve to corroborate the findings of Kaplan and Ruback (1995).     

Semiparametric hedonic price models: assessing the effects of agricultural nonpoint source pollution

In the area of environmental analysis using hedonic price models, we investigate the performance of various nonparametric and semiparametric specifications. The proposed model specifications are made up of two parts: a linear component for house characteristics and a non‐(semi)parametric component representing the nonlinear influence of environmental indicators on house prices. We adopt a general‐to‐specific search procedure, based on recent specification tests comparing the proposed specifications with a fully nonparametric benchmark model, to select the best model specification. An application of these semiparametric models to rural districts indicates that pollution resulting from intensive livestock farming has a significant nonlinear impact on house prices. Copyright © 2008 John Wiley & Sons, Ltd.     

SMOOTH QUANTILE‐BASED MODELING OF BRAND SALES,PRICE AND PROMOTIONAL EFFECTS FROM RETAIL SCANNER PANELS

Semiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods. Using retail store‐level data, we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy. We find that (a) flexible models with monotonicity constraints imposed on price effects dominate both in‐sample and out‐of‐sample comparisons while being robust even at the boundaries of the price distribution when data is sparse; (b) quantile‐based confidence intervals are much more accurate compared to least‐squares‐based intervals; (c) specifications reflecting that managers may not have exact knowledge about future competitive pricing perform extremely well. Copyright © 2013 John Wiley & Sons, Ltd.     

Goodness-of-fit tests in linear EV regression with replications

This paper proposes a goodness-of-fit test for checking the adequacy of parametric forms of the regression error density functions in linear errors-in-variables regression models. Instead of assuming the distribution of the measurement error to be known, we assume that replications of the surrogates of the latent variables are available. The test statistic is based upon a weighted integrated squared distance between a nonparametric estimator and a semi-parametric estimator of the density functions of certain residuals. Under the null hypothesis, the test statistic is shown to be asymptotically normal. Consistency and local power results of the proposed test under fixed alternatives and local alternatives are also established. Finite sample performance of the proposed test is evaluated via simulation studies. A real data example is also included to demonstrate an application of the proposed test.     

A prediction comparison of housing sales prices by parametric versus semi-parametric regressions

This study estimates a hedonic price function using a semi-parametric regression and compares the price prediction performance with conventional parametric models. This study utilizes a large data set representing 2595 single-family residential home sales between July 2000 and June 2002 from Pitt County, North Carolina. Data from Geographic Information Systems (GIS) are incorporated to account for locational attributes of the houses. The results show that the semi-parametric regression outperforms the parametric counterparts in both in-sample and out-of-sample price predictions, indicating that the semi-parametric model can be useful for measurement and prediction of housing sales prices.     

Measuring environmental effects on property values without hedonic regressions

The effect of environmental nuisances on property values has been frequently studied using hedonic regressions, yet that method has extensive data and estimation requirements. The technique developed in this paper uses repeat sales on houses to substantially reduce these requirements. The technique adjusts for depreciation between sales and allows for nonzero error covariance terms when a house has been sold more than twice. The effect of highway noise on property values is studied using this method, and the results are statistically equivalent to the results of an extensive hedonic study for the same area.     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

Semiparametric estimation of a hedonic price function

Previous work on the preferred specification of hedonic price models usually recommended a Box-Cox model. In this paper we note that any parametric model involves implicit restrictions and they can be reduced by using a semiparametric model. We estimate a benchmark parametric model which passes several common specification tests, before showing that a semiparametric model outperforms it significantly. In addition to estimating the model, we compare the predictions of the models by deriving the distribution of the predicted log(price) and then calculating the associated prediction intervals. Our data show that the semiparametric model provides more accurate mean predictions than the benchmark parametric model.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.