A Survey of Methods and Input Data Types for House Price Prediction

Abstract

Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning and tailored methods, which could guide further research.

Total Least Squares Estimation in Hedonic House Price Models

“…(2) It is used to provide automated valuations (or general appraisals) of properties [14], which is also a critical step in property tax determination in some countries (e.g., the United States and Germany). (3) It is used to explain house price variations or determine the impact of certain characteristics on houses, revealing house price drivers and mirroring the real estate market development stages [2]. Housing price drivers can be roughly divided into two categories.…”

“…The Hedonic Price Model (HPM) extends from the market approach [1,2], grounded in supply-demand theory, and uses regression analysis to relate characteristics to transaction prices. Studying the HPM in depth for parameter estimation and housing price prediction is crucial in facilitating well-informed decision-making, ensuring the integrity of real estate transactions, and conducting precise tax assessments [3].…”

Investigation of Real Estate Tax Leakage Loss Rates with ANNs

“…Some researchers include longitude and latitude values in the dataset or use location-sensitive (kriging, spatial econometric model (SEM), spatially varying coefficient (SVC), etc.) models to calculate spatial effects [70].…”

“…Buildings 2023, 13, x FOR PEER REVIEW 7 of 29 sensitive (kriging, spatial econometric model (SEM), spatially varying coefficient (SVC), etc.) models to calculate spatial effects [70].…”

Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real Estate Market

“…Such advanced approaches have the potential to provide deeper insights and more accurate predictions than traditional econometric models, which can struggle to capture the nuanced relationships and patterns inherent in housing market data [18]. By leveraging the capabilities of machine learning algorithms and hybrid approaches, researchers can gain a more complete understanding of the drivers and dynamics that shape housing markets [19][20][21][22][23][24][25][26]. For example, the study by Michele et al [24] uses machine learning techniques to address the problem of duplicate ads in online housing listings, which can skew the analysis of housing supply and demand.…”