A Novel Hybrid House Price Prediction Model

Süreyya Özöğür Akyüz (), Birsen Eygi Erdogan (), Özlem Yıldız () and Pınar Karadayı Ataş ()
Additional contact information
Süreyya Özöğür Akyüz: Bahçeşehir University
Birsen Eygi Erdogan: Marmara University
Özlem Yıldız: Bahçeşehir University
Pınar Karadayı Ataş: Arel University

Computational Economics, 2023, vol. 62, issue 3, No 15, 1215-1232

Abstract: Abstract The real estate sector is evolving and changing rapidly with the increase in housing demand, and new luxury housing projects appear every day. The reliability of housing market investments is largely dependent on accurate pricing.The aim of this study is to introduce a dynamic pricing procedure that estimates house prices using the most important characteristics of a house. For this purpose, a hybrid algorithm using linear regression, clustering analysis, nearest neighbor classification and Support Vector Regression (SVR) method is proposed. Our hybrid algorithm involves using the output of one method as the input of another method for home price prediction to deal with the heteroscedastic nature of the housing data. In other words, the aim of this study is to present a hybrid algorithm that will create different housing clusters from the available data set, classify the houses to which the cluster is unknown, and make price predictions by creating separate prediction models for each class. Housing data collected through manual web scraping of Kadıköy district in Istanbul were used for training and validation of the proposed algorithm. In addition to these data, we validated our algorithm on the KAGGLE house dataset, which covers a wide range of features. The results of the hybrid algorithm were compared using multiple linear regression, Lasso, ridge regression, Support Vector Regression (SVR), AdaBoost, decision tree, random forest and XGBoost regression. Experimental results show that the proposed hybrid model is superior in terms of both Residual Mean Square Error (RMSE), Mean Absolute Value Percent Error (MAPE) and adjusted Rsquare measures for both Kadıköy and KAGGLE housing dataset.

Keywords: Housing pricing; Support vector regression; K-means clustering; K-NN classification (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-022-10298-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:62:y:2023:i:3:d:10.1007_s10614-022-10298-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-022-10298-8

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().