Location-Centered House Price Prediction: A Multi-Task Learning Approach

Abstract

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, including transportation profile, education profile, suburb profile based on census data, and facility profile. Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness among partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and select specific MTL-based methods with different regularization terms to capture and exploit the relatedness among tasks. Based on real-world house transaction data collected in Melbourne, Australia, we design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Artificial intelligence algorithms to predict Italian real estate market prices

“…school zones and ranking), suburb profile based on census data, facility profile (e.g. nearby hospitals, supermarkets) (Gao et al, 2019). A Geographical Information System (GIS) based medium.…”

“…school zones and ranking), suburb profile based on census data, facility profile (e.g. nearby hospitals, supermarkets) (Gao et al, 2019). A Geographical Information System (GIS) based Artificial intelligence algorithms representation of the ANN's error helps prove that the network was able to learn the importance of the location in the training phase.…”

“…However, these methods often fail in capturing the complexity and the variety of the assets in the current RE market, thus the assessments are often characterised by a high inaccuracy (Zurada et al , 2006). Various RE stakeholders such as house owners, buyers, investors and agents use house price prediction frameworks usually depending on multiple factors; some of them are linked to the geometrical aspects of the physical assets, such as the volume and the area, whereas some other are related to the geographical position of the buildings within the Built Environment (Gao et al , 2019). Moreover, the general physical conditions and the assets' year of construction can further influence the final prices (Rahadi et al , 2015).…”

Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model

“…The definition of real estate in economics is mainly divided into narrow sense and broad sense. Real estate in the broad sense is understood as the sum of real estate commodity relations generated in the exchange process [ 8 ]; real estate in the narrow sense is understood as a place used for real estate rental, sale, mortgage, and other commodity transactions [ 9 ]. It is worth mentioning that the real estate price in this article is both an equilibrium price and a market price [ 10 ].…”

Fast local linear regression with anchor regularization

“…The regression problem is an important problem in machine learning, data mining, and statistics, and several research works have investigated it in the past decades. Examples include stock price prediction [12,25], age prediction from RNA-seq [6] or images [11], sentimental analysis [15,18], or house prediction [7] to name a few.…”