Spatial Hedonic Regression Models for Real Estate in Vienna City Region

2007 ◽  
Keyword(s):  
Real Estate ◽  
Regression Models ◽  
Hedonic Regression ◽  
City Region

scholarly journals Estimating Bargaining Power in Real Estate Pricing Models: Conceptual and Empirical Issues

2020 ◽  
Vol 13 (5) ◽  
pp. 105
Author(s):  
Steven B. Caudill ◽  
Franklin G. Mixon
Keyword(s):  
Real Estate ◽  
Bargaining Power ◽  
Regression Models ◽  
Hedonic Regression ◽  
Omitted Variables ◽  
Pricing Models ◽  
The Real Estate ◽  
Real Estate Prices ◽  
Relative Bargaining Power ◽  
Real Estate Economics

The relative bargaining power of the buyer and seller is a key feature of real estate pricing models. Classic real estate studies have sought to address bargaining effects in hedonic regression models. Prior research proposes a procedure to estimate bargaining effects in hedonic regression models that depends critically on a substitution to eliminate omitted variables bias. This study shows that the proposed solution that is often cited in the real estate economics literature does not solve the omitted variables problem given that both models are merely different parameterizations of the same model, and thus produces biased estimates of bargaining power when certain property characteristics are omitted. A classic hedonic regression model of real estate prices using Corsican apartment data supports our contention, even when the assumption of bargaining power symmetry is relaxed.

scholarly journals Impact of the Regularization of Regression Models on the Results of the Mass Valuation of Real Estate

2020 ◽  
Vol 20 (1) ◽  
pp. 163-176
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Multiple Regression ◽  
Ridge Regression ◽  
Regression Models ◽  
Real Estate Market ◽  
Data Set ◽  
Important Conclusion ◽  
Property Valuation ◽  
Multiple Regression Models ◽  
Training Sets

AbstractResearch background: Mass appraisal is a process in which multiple properties are appraised simultaneously, with a uniform approach. One of the tools that can be used in this area are multiple regression models. In the valuation of real estate features are often described on an ordinal or nominal scale. Replacing them with dummy variables with an insufficient number of observations leads to multicollinearity. On the other hand, there is a risk of overfitting the model. One of the ways to eliminate or weaken these phenomena is to introduce regularization based on a model’s penalization for the high values of its weights.Purpose: The aim of the study is to verify the hypothesis whether regularized regression reduces the errors of property valuation and which of the analyzed methods is the most effective in this context.Research methodology: The article will present a study in which two ways of regularization will be applied – ridge and lasso regression, in the context of their impact on the errors of property valuation. The analyzed data set includes over 300 land properties valued by property appraisers. The key aspects of the study are the selection of optimal values of the regularization parameter and its influence on model’s errors with a different number of observations in the training sets.Results: The study showed that regularization improves valuation results and, more specifically, allows for lower average absolute percentage errors. The improvement of model effectiveness was more pronounced in the case of ridge regression. An important result is also that regularization has provided a higher accuracy of valuation compared to multiple regression models for smaller training sets.Novelty: The article confirms the effectiveness of regularization as a way to eliminate the problem of multicollinearity or overfitting of the model. The results showed that ridge regression can be an effective way of modelling the value of real estate. Especially in the case of a small amount of market data, which is an important conclusion in the context of the real estate market.

scholarly journals REVEALING THE INVESTMENT VALUE OF PENANG HERITAGE PROPERTIES

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Tiong Cheng Chin ◽  
Bin Tan Yan ◽  
Fang Wong Wai ◽  
Seng Lai Kong ◽  
Yu Xuan Koh
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Regression Method ◽  
Hedonic Regression ◽  
Social Functions ◽  
Diversification Strategy ◽  
Aesthetic Quality ◽  
Alternative Investments ◽  
Property Price ◽  
Heritage Buildings

Heritage buildings are a representation of historic features and the Malaysian culture. The intangible value of a heritage property comprises aesthetic quality, spiritual aspects, social functions, and its own uniqueness. Therefore, heritage properties have been seen to be moving away from traditional alternative investments, which are not covered by conventional real estate schemes. Additionally, the characteristics of heritage properties are expected to be seen as ‘art’, and they offer a highly beneficial diversification strategy with a relatively low correlation towards traditional assets classes. The Penang (Island) Heritage Property Price Index (PPHPPI) is estimated to be using a hedonic regression method. Based on the index, the heritage property records the highest quarterly returns and risk among the conventional assets considered in this study.

scholarly journals Hedonic regression models for Tokyo condominium sales

2016 ◽  
Vol 60 ◽  
pp. 300-315 ◽  
Author(s):  
W. Erwin Diewert ◽  
Chihiro Shimizu
Keyword(s):  
Regression Models ◽  
Hedonic Regression

scholarly journals Valuation methods for the housing market: Evidence from Budapest

2016 ◽  
Vol 66 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Dávid Kutasi ◽  
Milán Csaba Badics
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Large Scale ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Hedonic Regression ◽  
Valuation Methods ◽  
Valuation Method

Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

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