Spatial Prediction of House Prices Using Lpr and Bayesian Smoothing PDF Download
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Author: John M. Clapp
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper is motivated by the limited ability of hedonic price equations to deal with spatial variation in house prices. Host (1999) divides spatial processes into low and high frequency components, inspiring the methods developed here. We further divide Host's low frequency spatial patterns into truly low frequency components, typically modeled parametrically with distance to the CBD or other points of interest, and medium frequency components, modeled here non-parametrically, with local polynomial regressions (LPR). Host, on the other hand, uses LPR for both low and medium frequency variation. LPR gives sufficient flexibility to find substantial spatial variation in house values.We adopt a partially Bayesian approach to modeling high frequency spatial association. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. It allows for prediction at sampled or unsampled locations as well as prediction interval estimates.Out-of-sample mean squared error and related statistics validate the proposed methods. The model is shown to provide insights into the spatial variation of house value.
Author: John M. Clapp
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper is motivated by the limited ability of hedonic price equations to deal with spatial variation in house prices. Host (1999) divides spatial processes into low and high frequency components, inspiring the methods developed here. We further divide Host's low frequency spatial patterns into truly low frequency components, typically modeled parametrically with distance to the CBD or other points of interest, and medium frequency components, modeled here non-parametrically, with local polynomial regressions (LPR). Host, on the other hand, uses LPR for both low and medium frequency variation. LPR gives sufficient flexibility to find substantial spatial variation in house values.We adopt a partially Bayesian approach to modeling high frequency spatial association. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. It allows for prediction at sampled or unsampled locations as well as prediction interval estimates.Out-of-sample mean squared error and related statistics validate the proposed methods. The model is shown to provide insights into the spatial variation of house value.
Author: Anthony Owusu-Ansah
Publisher: Routledge
ISBN: 1351590995
Category : Business & Economics
Languages : en
Pages : 222
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Book Description
The importance of house prices to households, real estate developers, banks and policy-makers cannot be overemphasised. House price changes affect consumer spending and business investment patterns, which in turn affect the wider macro economy and the entire business cycle. Measuring and understanding house prices is therefore essential to a functioning economy, but researchers continue to disagree on the best methodological approach for constructing real estate indices. This book argues the need for more accurate house price indices, outlines the various methods used to construct indices and discusses the existing house price indices around the globe. It shows how the raw data of property transactions can be prepared for the purpose of constructing indices, discusses various applications of property price indices and empirically demonstrates how the index numbers can be used to model the supply of new houses and to estimate the price elasticity of supply. Essential reading for economists, real estate professionals and researchers, and policy-makers.
Author: Rennard, Jean-Philippe
Publisher: IGI Global
ISBN: 1591409853
Category : Business & Economics
Languages : en
Pages : 1066
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Book Description
"This book provides applications of nature inspired computing for economic theory and practice, finance and stock-market, manufacturing systems, marketing, e-commerce, e-auctions, multi-agent systems and bottom-up simulations for social sciences and operations management"--Provided by publisher.
Author: Yujing Zheng
Publisher:
ISBN:
Category : Housing
Languages : en
Pages : 66
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Book Description
An accurate prediction to the housing prices is very important to all the real estate market participants: homeowners, mortgage lenders, land agents, investors, real estate appraisers, and insurers. Regression analysis is the most widely used modeling technique to determine the relativeness and strength of the relationship between the response variable and explanatory variables. In this project, we focus on the comparison of different regression methods, including the ordinary least squares method, penalized least squares, and univariate and bivariate smoothing techniques, for predicting the real estate market.
Author:
Publisher:
ISBN:
Category : Electronic publications
Languages : en
Pages : 17
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Book Description
This work proposes a non-stationary random field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is t using a Bayesian approach via a Markov chain Monte Carlo algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model.
Author: Yunlong Gong
Publisher:
ISBN: 9789492516510
Category :
Languages : en
Pages :
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Book Description
This research discovers the spatial regularities of house prices across Chinese prefecture cities in an economic common area and investigates the underlying formation process. It reveals an uneven distribution of house prices across cities, with those large and/or higher-tier cities and their neighbours having significantly higher house prices. Such an uneven pa����ern of house prices demonstrates the agglomeration spillovers in the interurban housing market. Two forms of spillovers are empirically examined. The first is the urban hierarchy distance effect, which is related to the position of a city in a hierarchical urban system. In general, the distance penalty of higher-tier urban centres is confirmed, that is, all else being equal, the further away a city is from the higher-tier city, the lower the house price. The second form of spillovers relates to a city's position in a city network system, in which no hierarchical structure is imposed. In such a situation, the spillovers arise from the interaction with neighbouring cities and it is found that a city that has larger neighbours tends to have higher house prices. These two forms of spillovers are somewhat correlated with each other because a higher-tier city is always associated with a larger urban size.
Author: Haoying Meng
Publisher:
ISBN: 9781369311204
Category :
Languages : en
Pages :
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Book Description
House prices are of interest to the general public and government agencies for many reasons. The complexity and practicality of house price modeling have attracted many researchers. In this dissertation, attempts are made to explore the dependence structure in time and space among houses using over 130 thousand house price observations in San Jose from 1991 to 2012. Innovative spline methods are utilized to build a forecasting model incorporating both hedonic, spatial and temporal information. The use of splines greatly reduces the number of variables needed in the model without sacrificing for precision. Moreover, the recession period (2008--2010) was given special care because it behaved differently from the rest of the 22 year time period. The model proposed in this dissertation uses both repeat sales and single sale transactions, and is able to produce an overall price index for the whole region, as well as predictions for individual houses. The final model, which includes an autoregressive spatio-temporal error term, is shown to have better predictive abilities than other competing methods in the literature.
Author: Alfred Teye
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
Following the 2007-08 Global Financial Crisis, there have been a growing research interest on the spatial interrelationships between house prices in many countries. This paper examines the spatio-temporal relationship between house prices in the twelve provinces of the Netherlands using a recently proposed econometric modelling technique called Bayesian graphical vector autoregression (BG-VAR). This network approach enables a data driven identification of the most dominant provinces where house price shocks may largely diffuse through the housing market and it is suitable for analysing the complex spatial interactions between house prices. Using temporal house price volatilities for owner-occupied dwellings, the results show evidence of house price diffusion pattern in distinct sub-periods from different provincial housing submarkets in the Netherlands. We observed particularly prior to the crisis, diffusion of temporal house price volatilities from Noord-Holland.
Author: xiaolong Liu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This paper incorporates spatial and temporal dependence among housing transactions in predicting future house prices. We employ the spatial and temporal autoregressive model and structure the spatial and temporal weighting matrices as in Pace et al. (1998). We control for the time variation of both the attribute prices and the spatial and temporal dependence parameters through performing the analysis on an annual basis. Spatial heterogeneity is accounted for using experience-based definition of submarkets by real estate professionals. Using a comprehensive housing transaction data set from the Dutch Randstad region, we show that integrating the spatial and temporal dependence within the hedonic modeling improves the prediction power as compared to traditional hedonic model that neglects these effects.
Author: Andrew Caplin
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
Economists do not have reliable measures of current house values, let alone housing returns. This ignorance underlies the illiquidity of mortgage-backed securities, which in turn feeds back to deepen the sub-prime crisis. Using a massive new data tape of housing transactions in L.A., we demonstrate systematic patterns in the error associated with using the ubiquitous repeat sales methodology to understand house values. In all periods, the resulting indices under-predict sales prices of less expensive homes, and over-predict prices of more expensive homes. The recent period has produced errors that are not only unprecedentedly large in absolute value, but highly systematic: after a few years in which the indices under-predicted prices, they now significantly over-predict them. We introduce new machine learning techniques from computer science to correct for prediction errors that have geographic origins. The results are striking. Accounting for geography significantly reduces the extent of the prediction error, removes many of the systematic patterns, and results in far less deterioration in model performance in the recent period.
