Pre-owned housing price index forecasts using Gaussian process regressions

Bingzi Jin · Xiaojie Xu

179 citations0/0 files storedJournal of Modelling in Management

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Housing Market and Economics Forecasting Techniques and Applications Stock Market Forecasting Methods

Abstract

Purpose The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors. Design/methodology/approach This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation. Findings The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%. Originality/value The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 179 citations exist upstream.

References

1996A test for independence based on the correlation dimension 1997Testing the equality of prediction mean squared errors 1978Hedonic housing prices and the demand for clean air 2011Convergence rates of efficient global optimization algorithms 2020Corn Cash Price Forecasting 2020Predicting property prices with machine learning algorithms 2021Corn cash price forecasting with neural networks 2015A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units

Cited by

2025Predictions of residential property price indices for China via machine learning models 2024Machine learning-based forecasts of residential property prices in Hangzhou City, Zhejiang Province, China 2025Forecasts of wholesale food price indices through Gaussian process regressions 2025Machine learning natural gas price predictions 2025Chinese energy security index price forecasting through the neural network 2024Regional steel price index predictions for the southwest Chinese market through machine learning

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.