Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2

Series Vol. 6 , 27 April 2023


Open Access | Article

House Price Prediction with Big Data

Ziyu Cui * 1
1 Santa Clara University, Santa Clara, CA 95053, United States

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 6, 85-93
Published 27 April 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ziyu Cui. House Price Prediction with Big Data. AEMPS (2023) Vol. 6: 85-93. DOI: 10.54254/2754-1169/6/20220155.

Abstract

Many of the current models for house price prediction focus on the house price index itself or include too few factors. In this research, however, different aspects of housing properties (bedrooms, bathrooms, grade, view, etc.) will be incorporated to generate a more accurate prediction model. Doing so not only remedies for a lack of variables in current models but also benefits the consumers by providing an accurate estimation of housing values. This paper examines the estimation methodology of a multiple linear regression model. To get the optimal prediction power and keep the model as simple as possible, different variable combinations will be tested. Through comparing different regression models and analyzing the regression results, the predictive model introduced in this paper has a high house price prediction power. This paper provides a potential solution for the prediction of the house price in the King County, Washington.

Keywords

House Price Prediction, Predictive Model, Data Analysis, Linear Regression, Pricing

References

1. Hollas, D., et al.: Zillow’s Estimates of Single-Family Housing Values: Semantic Scholar. Zillow’s Estimates of Single-Family Housing Values. Semantic Scholar. (1970). https://www.semanticscholar.org/paper/Zillow-%E2%80%99-s-Estimates-of-Single-Family-Housing-Hollas-Rutherford/ca1a7e03f08380dce18ff5f766f1bd4301a42201#paper-header.

2. Harlfoxem. House Sales in King County, USA. Kaggle. (2016). https://www.kaggle.com/datasets/harlfoxem/housesalesprediction.

3. Spriestersbach, A., et al.: Descriptive Statistics: The Specification of Statistical Measures and Their Presentation in Tables and Graphs. Part 7 of a Series on Evaluation of Scientific Publications. Deutsches Arzteblatt International, Deutscher Arzte Verlag. (2009). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2770212/.

4. Ambarish, B.: Tutorial Housesales Kingcounty EDA&Modelling. Kaggle. (2018). https://www.kaggle.com/code/ambarish/tutorial-housesales-kingcounty-eda-modelling/report.

5. Dhakre, A.: EDA: Linear Regression: K-Fold CV: ADJ R2=0.87. Kaggle. (2017). https://www.kaggle.com/code/amitdhakre13/eda-linear-regression-k-fold-cv-adj-r2-0-87.

6. Marill, K. A.: Advanced Statistics: Linear Regression, PART II ... - Wiley Online Library. Wiley Online Library. https://onlinelibrary.wiley.com/doi/pdf/10.1197/j.aem.2003.09.006.

7. Heyman, A. V., Sommervoll, D. E.: House Prices and Relative Location. ScienceDirect. (2019). https://www.sciencedirect.com/science/article/pii/S0264275118312241.

8. Rivas, R., Patil, D., Hristidis, V. et al.: The impact of colleges and hospitals to local real estate markets. J Big Data 6, 7 (2019). https://doi.org/10.1186/s40537-019-0174-7.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2
ISBN (Print)
978-1-915371-23-2
ISBN (Online)
978-1-915371-24-9
Published Date
27 April 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/6/20220155
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated