Determinants of House Price: A Decision Tree Approach

Abstract

The hedonic-based regression approach has been utilised extensively to investigate th relationship between house prices and housing characteristics. However, this approach is subject t criticisms arising from potential problems relating to fundamental model assumptions an estimation such as the identification of supply and demand, market disequilibrium, the selectio of independent variables, the choice of functional form of hedonic equation and marke segmentation. This study introduces and utilises an alternative approach-the decision tre approach, which is an important statistical pattern recognition tool. Using the Singapore resal public housing market as a case study, the article demonstrates the usefulness of this techniqu in examining the relationship between house prices and housing characteristics, identifying th significant determinants of housing prices and predicting housing prices. The built tree show that homebuyers are more concerned about the basic housing characteristics of two- and three room flats or four-room flats such as floor area, model type and flat age. However, homebuyer of five-room flats pay more attention to floor level in addition to the basic housin characteristics. In addition, homebuyers of executive apartments are less concerned about basi quantitative characteristics and have higher housing consumption expectations and pay mor attention to 'quality' and service characteristics such as recreational facilities and the livin environment.

Mass Appraisal of Residential Real Estate Using Multilevel Modelling

“…We obtain a pseudo-R 2 of 0.867, a very satisfactory level if we compare it with that obtained in previous valuation studies (Fan et al 2006;Selim 2009). No further neighbourhood related variables need be added to improve the valuation model because the improvement range is very low.…”

“…The former includes regression models and their multiple variants. The second group is well represented in the literature and includes approaches like decision trees (Fan et al 2006), rough set theory (D'Amato 2007), artificial neural networks (Tay and Ho 1991;García et al 2008;Selim 2009), support vector machines (Kontrimas and Verikas 2011) and random forest (Antipov and Pokryshevskaya 2012).…”

Fundamental Determinants of Real Estate Prices: A Panel Study of German Regions

“…Other studies have extended the hedonic house price regression model by incorporating additional factors such as a house's view (Benson et al 1998) or environmental influences (Dickes and Crouch 2015) or by methodological aspects such as analysing heteroscedasticity in hedonic models (Stevenson 2004). In the literature, alternative approaches to hedonic-based regression analysis of real estate price determinants have been proposed, among them artificial neural network modelling (Nguyen and Cripps 2001) or the decision tree approach (Fan et al 2006), but hedonic-type regressions have remained one of the workhorse models for house price analysis in science and for practitioners (Bao and Wan 2007). …”

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

“…Fan et al [12] used the decision tree technique for exploring the relationship between house prices and housing characteristics, which aided the determination of the most important variables of housing prices and predicted housing prices. In recent studies, there have also been other examples based on recent machine learning techniques, such as support vector machines (SVM) [13].…”