MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data

TL;DR

This paper introduces the newly launched project "Monopoly" (named after a classic board game) in which a distributed approach for revaluing private properties by learning to price public facilities with the large-scale urban data accumulated via Baidu Maps and shows that the approach outperforms several mainstream methods with significant margins.

Abstract

The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ``Monopoly'' (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals etc.) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ``Monopoly'' is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.

Figures (3)

• Figure 1: A screenshot of the housing prices of the Haidian District in Beijing. We can see that there are three kinds of dash circles colored by red, blue, and orange. Red circle: the average price of the properties near the Xibeiwang Station of No.16 Metro is $79,772~CNY/m^2$. Blue circle: the average price of the properties near the Zhongguancun No. 2 Primary School (Baiwang Campus) is $82,216~CNY/m^2$. Orange circle: however, the average price of the properties without those public facilities nearby is $70,744~CNY/m^2$, noticeably lower than the two other areas.
• Figure 2: An illustration of the organization of urban data employed by the "Monopoly" project. To be specific, our approach regards many points of interest (POIs) as nodes in an undirected weighted graph based on their geographic information. Then we formulate the factors, including the variables that indicate the values of surrounding public facilities, to parallelly regress to the housing prices we know. As a result, the estimated values of both public facilities and private properties can be updated iteratively until convergence.
• Figure 3: An illustration on the average performance (i.e., mean $\pm$ standard deviation) of "Monopoly" measured by MAE and RMSE, along with different values (i.e., $0.5$ km, $1.0$ km, $3.0$ km, and $5.0$ km) of influencing radius.