A roadmap to determine the important factors of the house value: a case study by using actual price registration data of Taipei housing transactions

Abstract

While many studies have applied data mining techniques to judge housing prices, few have decoded the important attributes or prioritized them simultaneously. This paper aims to utilize five data mining techniques to discover the important attributes for three major types of real estate in Taipei city. The datasets, involving a total of 22,480 transactions, were publicly available from the Taiwan Actual Price Registration from July 2013 to August 2015. The five models are decision trees, random forests, model trees, artificial neural networks and multiple regression. The criteria used to measure the forecasting accuracy are MAPE, R², RMSE, MAE and COR. The model with the best performance for all houses is the Model Tree with a MAPE value of 27.59. As for apartments, the best is Random Forests. Artificial Neural Networks perform best for suites and buildings with elevators. Different housing types need different models. Furthermore, the attributes importance helps us to conclude the really critical attributes, which include the floor area, administrative districts, parking area and land area, and their rankings. This variable ranking and selection procedure proposed by this research can also be adopted to improve the prediction efficiency for most big data applications other than the housing transactions.

The Effect of the Principal Component Index for Housing Quality Satisfaction on Housing Price: Urban vs. Rural Analysis

“…2014), such as floor area (Guttery, 2002;Sirmans et al, 2005;Wang & Chen, 2018) and total number of floors (Colwell et al, 1998;Dermisi & McDonald, 2010), but building area squared has a negative relationship with housing prices (Fisher et al, 2006). Wang and Chen (2018) apply data mining to determine housing prices, and find that the factors affecting housing prices include the area of the house, house age, number of floors, and number of rooms (Aliyev et al, 2019;Keskin, 2008;Chang et al, 2018).…”

“…Other factors affecting housing prices include fluctuation trends of the real estate market (Wu et al, 2017;Hui, 2013;Bates & Santerre, 2016;Miao et al, 2020;Annette et al, 2018), noise, air quality, homebuyer identity and their psychological behavior and homebuying preferences, length of residence, average household income, neighbor satisfaction (Zangerle, 1927), earthquake-related risks in the area, access to administrative and parking areas (Wang & Chen, 2018), housing location, maintenance level, number of sales (Aliyev et al, 2019), and access to public services (Li et al, 2019). In addition to housing characteristics and convenience in daily life, other factors of influence are the real estate cycle (Wu et al, 2017;Hong et al, 2015;Hui, 2013;Bates & Santerre, 2016;Miao et al, 2020;Annette et al, 2018), noise conditions (Bré card et al, 2018), real estate market environment, homebuyer identity and their psychological behavior and homebuying preferences (Chen et al, 2012), urban infrastructure (Liu et al, 2020), housing affordability (Seo et al, 2018), socioeconomic characteristics, neighborhood quality, and location factors (Keskin, 2008).…”