An Integrated Model for Urban Subregion House Price Forecasting: A Multi-source Data Perspective

Ge, Chuancai; Wang, Yang; Xie, Xike; Liu, Hengchang; Zhou, Zhengyang

doi:10.1109/icdm.2019.00123

Cited by 14 publications

(15 citation statements)

References 15 publications

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“…The econometric models involve time-series analysis such as Box Jenkins's autoregressive integrated moving average (ARIMA), generalised autoregressive conditional heteroskedastic (GARCH) (Kim, 2004), vector autoregressive model (VAR) (Ge et al, 2019) and other spatial analysis models. A distinction between these models is that VAR would not perform well if a huge number of time-series is involved (van de Minne et al, 2021), while ARIMA and GARCH are capable of processing a large number of time series.…”

Section: Literature Review 21 Modelling Techniques In Real Estate Pri...mentioning

confidence: 99%

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An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach

Yakub

Achu

Ali

et al. 2022

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PurposeThere are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.Design/methodology/approachUsing the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).FindingsThe study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.Practical implicationsThe results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.Originality/valueThe “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.

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Section: Literature Review 21 Modelling Techniques In Real Estate Pri...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach

Yakub

Achu

Ali

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, these transactions are dispersedly distributed in spatial and temporal domains, which induces spatiotemporal asynchronism. Such asynchronously spatiotemporal dependencies among real estate transactions also distinguish our task from existing works on predicting regional future house prices [9,35], where the input are more regular time series data. It also prevents us to adopt existing spatiotemporal prediction approaches [20] for our task.…”

Section: Event-level Representation Learningmentioning

confidence: 99%

“…This work is also related to real estate forecasting tasks. For example, Tan et al [35] proposes a time-aware latent hierarchical model and Ge et al [9] proposes an integrated framework that improving the DenseNet to predict future house prices of regions. Zhu et al [46] proposes a multi-task linear regression model for real estate's days-on-market prediction.…”

Section: Related Workmentioning

confidence: 99%

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Zhang

Líu

Zha

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a nontrivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to comprehensively profile the real estate from multiple perspectives (e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features. CCS CONCEPTS• Information systems → Data mining; Spatial-temporal systems; • Applied computing → Marketing.

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“…The choice of the NN technique here takes into consideration its demonstrated usefulness and potential for real estate price forecasting in the literature and the fact that the office property price indices considered here show nonlinear patterns. Our analysis could help expand knowledge of applying machine learning tools to forecast office property prices in the substantially growing Chinese market as previous research, in general, concentrates on one specific city when investigating other types of properties (Xin et al, 2004;Lam et al, 2008;Wang et al, 2014;Ma et al, 2015;Li et al, 2018;Yu et al, 2018;Fu, 2018;Liu and Liu, 2019;Piao et al, 2019;Ge et al, 2019;Li et al, 2020;Yan and Zong, 2020;Ho et al, 2021). The coverage of the major cities, with availabilities of data, should represent an economic natural way to investigate the forecasting issue for office properties as these cities are where demand and supply are most active and each of them could have different price characteristics that are worth exploring.…”

Section: Introductionmentioning

confidence: 99%

Office property price index forecasting using neural networks

Zhang

2023

JFMPC

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Purpose The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

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An Integrated Model for Urban Subregion House Price Forecasting: A Multi-source Data Perspective

Cited by 14 publications

References 15 publications

An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach

An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Office property price index forecasting using neural networks

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