Modeling of the effect of transportation system accessibility on residential real estate prices: The case of Washington metropolitan area, USA

IF 2.4 Q3 TRANSPORTATION
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Abstract

Deep learning accurate predictions of house prices are essential for prospective homeowners, investors, appraisers, and insurers. However, some studies lack accuracy as they overlook critical factors like accessibility and economic attributes that influence house prices. This paper aims to predict house prices by considering structural, locational, accessibility, and economic attributes, while also exploring the effect of accessibility on housing prices. The dataset contains 2,019,663 real estate transaction records from 1975 to 2018 in the Washington metropolitan area, obtained from the Zillow website. In this study, the accessibility index is calculated using Distance, Cumulative Opportunities, and Gravity measures, with the gravity measure surpassing others due to its consideration of both land use and transportation aspects. Economic attributes are then utilized to predict the average monthly house price using deep learning algorithms such as LSTM, GRU, and Simple RNN, with the Simple RNN demonstrating superior performance. Following the amalgamation of structural and locational attributes with the accessibility index and average house prices, various machine learning algorithms—including Linear Regression, Lasso, Ridge, Random Forest, GBM, LightGBM, XGBoost, Decision Tree, AdaBoost, Artificial Neural Network, and Stacked Generalization—are employed for prediction. Subsequent evaluation reveals that Stacked Generalization (ANN + LightGBM) provides the best performance, with an R-squared value of 0.96 and RMSE of $23,290. Moreover, this paper identifies accessibility index thresholds (80,003 for large buildings and 160,103 for small buildings) and demonstrates that a higher accessibility index leads to lower housing prices, attributed to noise pollution, decreased privacy, and increased supply responses.

交通系统可达性对住宅房地产价格影响的建模:美国华盛顿大都会区案例
深度学习对房价的准确预测对于未来的房主、投资者、评估师和保险公司来说至关重要。然而,一些研究由于忽略了影响房价的交通便利性和经济属性等关键因素,因而缺乏准确性。本文旨在通过考虑结构、位置、可达性和经济属性来预测房价,同时探索可达性对房价的影响。数据集包含 1975 年至 2018 年华盛顿大都会地区的 2,019,663 条房地产交易记录,这些记录来自 Zillow 网站。在本研究中,使用距离、累积机会和重力测量法计算可达性指数,其中重力测量法由于同时考虑了土地使用和交通方面的因素而优于其他测量法。然后利用经济属性,使用 LSTM、GRU 和 Simple RNN 等深度学习算法预测月平均房价,其中 Simple RNN 的性能更优。将结构和位置属性与可达性指数和平均房价合并后,采用了各种机器学习算法(包括线性回归、Lasso、Ridge、随机森林、GBM、LightGBM、XGBoost、决策树、AdaBoost、人工神经网络和堆叠泛化)进行预测。随后的评估显示,堆叠泛化(ANN + LightGBM)的性能最佳,R 方值为 0.96,RMSE 为 23,290 美元。此外,本文还确定了可达性指数阈值(大型建筑为 80 003,小型建筑为 160 103),并证明可达性指数越高,房价越低,这归因于噪音污染、隐私减少和供应增加。
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来源期刊
CiteScore
5.00
自引率
12.00%
发文量
222
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