Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

by   Hao Peng, et al.

We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.



There are no comments yet.


page 11

page 14


Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model

Electricity price is a key factor affecting the decision-making for all ...

Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data

Event ticket price prediction is important to marketing strategy for any...

Friddy multiagent price stabilization model

In a multiagent network model consisting of nodes, each network node has...

Housing Market Forecasting using Home Showing Events

Both buyers and sellers face uncertainty in real estate transactions in ...

Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism

In electricity markets, locational marginal price (LMP) forecasting is p...

A rapidly updating stratified mix-adjusted median property price index model

Homeowners, first-time buyers, banks, governments and construction compa...

A model for predicting price polarity of real estate properties using information of real estate market websites

This paper presents a model that uses the information that sellers publi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.