research-article

Open access

Towards explainable real estate valuation via evolutionary algorithms

Authors:

Sebastian Angrick,

Maximilian Kleissl,

Louise Molitor,

Tobias Friedrich,

Maximilian KatzmannAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 1130 - 1138

https://doi.org/10.1145/3512290.3528801

Published: 08 July 2022 Publication History

Abstract

Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as real estate valuation. Unfortunately, the methods applied there often exhibit a trade-off between accuracy and explainability.

One explainable approach is case-based reasoning (CBR), where each decision is supported by specific previous cases. However, such methods can be wanting in accuracy. The unexplainable machine learning approaches are often observed to provide higher accuracy but are not scrutable in their decision-making.

In this paper, we apply evolutionary algorithms (EAs) to CBR predictors in order to improve their performance. In particular, we deploy EAs to the similarity functions (used in CBR to find comparable cases), which are fitted to the data set at hand. As a consequence, we achieve higher accuracy than state-of-the-art deep neural networks (DNNs), while keeping interpretability and explainability.

These results stem from our empirical evaluation on a large data set of real estate offers where we compare known similarity functions, their EA-improved counterparts, and DNNs. Surprisingly, DNNs are only on par with standard CBR techniques. However, using EA-learned similarity functions does yield an improved performance.

References

[1]

Bruno Afonso, Luckeciano Melo, Willian Oliveira, Samuel Sousa, and Lilian Berton. 2019. Housing Prices Prediction with a Deep Learning and Random Forest Ensemble. In ENIAC. 389--400.

[2]

Charu C Aggarwal, Alexander Hinneburg, and Daniel A Keim. 2001. On the Surprising Behavior of Distance Metrics in High Dimensional Space. In ICDT. 420--434.

[3]

Sebastian Angrick, Ben Bals, Niko Hastrich, Maximilian Kleissl, Jonas Schmidt, Vanja Doskoc, Maximilian Katzmann, Louise Molitor, and Tobias Friedrich. 2021. Towards Explainable Real Estate Valuation via Evolutionary Algorithms. CoRR abs/2110.05116 (2021). arXiv:2110.05116 https://arxiv.org/abs/2110.05116

[4]

Sercan Ö Arık and Tomas Pfister. 2021. TabNet: Attentive Interpretable Tabular Learning. In AAAI. 6679--6687.

[5]

Alejandro Baldominos, Iván Blanco, Antonio José Moreno, Rubén Iturrarte, Óscar Bernárdez, and Carlos Afonso. 2018. Identifying Real Estate Opportunities Using Machine Learning. Appl. Sci. 8, 11 (2018).

[6]

Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger. 1990. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In ICDEM. 322--331.

Digital Library

[7]

Robert Carbone and Richard L Longini. 1977. A Feedback Model for Automated Real Estate Assessment. Manage. Sci. 24, 3 (1977), 241--248.

Digital Library

[8]

Marjan Čeh, Milan Kilibarda, Anka Lisec, and Branislav Bajat. 2018. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments. ISPRS Int. J. Geo-Inf. 7, 5 (2018), 168.

[9]

Joe Celko. 2015. Basic Aggregate Functions. In SQL for Smarties. Elsevier.

[10]

T. Dimopoulos, H. Tyralis, N. P Bakas, and D. Hadjimitsis. 2018. Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus. Adv. Geosci. 45 (2018), 377--382.

[11]

Matthew F Dixon, Igor Halperin, and Paul Bilokon. 2020. Machine Learning in Finance. Springer.

[12]

Diego S Ferreira. 2017. Neural Network Model for House Prices (Keras). Kaggle (2017). https://www.kaggle.com/diegosiebra/neural-network-model-for-house-prices-keras.

[13]

Saya Fujino, Naoki Mori, and Keinosuke Matsumoto. 2017. Deep convolutional networks for human sketches by means of the evolutionary deep learning. In IFSA-SCIS. 1--5.

[14]

Megan Garcia. 2016. Racist in the Machine: The Disturbing Implications of Algorithmic Bias. World Policy J. 33, 4 (2016), 111--117. muse.jhu.edu/article/645268

[15]

Gabrielle Gayer, Itzhak Gilboa, and Offer Lieberman. 2007. Rule-Based and Case-Based Reasoning in Housing Prices. B. E. J. Theor. Econ. 7 (2007).

[16]

Itzhak Gilboa, Offer Lieberman, and David Scheidler. 2011. A similarity-based approach to prediction. J. Econom. 162, 1 (2011), 124--131.

[17]

Vincenzo Del Giudice, Pierfrancesco De Paola, and Fabiana Forte. 2017. Using Genetic Algorithms for Real Estate Appraisals. Buildings 7, 2 (2017).

[18]

Brano Glumac and François Des Rosiers. 2018. Real Estate and Land Property Automated Valuation Systems: A Taxonomy and Conceptual Model. LISER Working Paper Series 9 (2018).

[19]

Sharad Goel, Justin M. Rao, and Ravi Shroff. 2016. Personalized Risk Assessments in the Criminal Justice System. Am. Econ. Rev. 106, 5 (2016), 119--23.

[20]

Marko Kryvobokov. 2007. What location attributes are the most important for market value? Prop. Manag. 25, 3 (2007), 257--286.

[21]

Tomo Lazovich. 2020. Does Deep Learning Have Politics?. In Neurips Resistance AI Workshop.

[22]

LIFULL Co., Ltd. 2019. LIFULL HOME'S Data Set. https://www.nii.ac.jp/dsc/idr/en/lifull/.

[23]

Yuqiao Liu, Yanan Sun, Bing Xue, Mengjie Zhang, and Gary G Yen. 2021. A Survey on Evolutionary Neural Architecture Search. IEEE Trans. Neural Netw. Learn. Syst. PP (2021).

[24]

Roy Lowrance. 2015. Predicting the Market Value of Single-Family Residential Real Estate.

[25]

Luckeciano Melo. 2019. NN With Tabular Data - Baseline. Kaggle (2019). https://www.kaggle.com/luckeciano/nn-with-tabular-data-baseline.

[26]

Erfan Miahi, Seyed A Mirroshandel, and Alexis Nasr. 2019. Genetic Neural Architecture Search for automatic assessment of human sperm images. CoRR (2019).

[27]

Russell B. Millar. 2011. Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB. John Wiley & Sons.

[28]

Poppy Noor. 2020. Can we trust AI not to further embed racial bias and prejudice? Br. Med. J. 368 (2020).

[29]

Cathy O'Neil. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

[30]

Nkolika J Peter, Hilary I Okagbue, Emmanuela C. M Obasi, and Adedotun O Akinola. 2020. Review on the Application of Artificial Neural Networks in Real Estate Valuation. Int. J. Adv. Trends Comput. Sci. Eng. 9, 3 (2020), 2918--2925.

[31]

D T Pham. 1970. Neural networks in engineering. WIT Trans. Ecol. Environ. 6 (1970).

[32]

Alvin Rajkomar, Jeffrey Dean, and Isaac Kohane. 2019. Machine Learning in Medicine. N. Engl. J. Med. 380, 14 (2019), 1347--1358.

[33]

M Mohan Raju, RK Srivastava, Dinesh Bisht, HC Sharma, and Anil Kumar. 2011. Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge. Adv. Artif. Intell. 2011 (2011), 1--11.

Digital Library

[34]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2021. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. Comput. Surv. 54, 4 (2021), 1--34.

[35]

Hajime Seya and Daiki Shiroi. 2021. A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network. Geogr. Anal. (2021).

[36]

Moshe Sipper, Randal Olson, and Jason Moore. 2017. Evolutionary computation: The next major transition of artificial intelligence? BioData Min. 10 (2017), 26.

[37]

Yanan Sun, Bing Xue, Mengjie Zhang, Gary G Yen, and Jiancheng Lv. 2020. Automatically Designing CNN Architectures Using the Genetic algorithm for Image Classification. IEEE Trans. Cybern. 50, 9 (2020), 3840--3854.

[38]

Agostino Valier. 2020. Who performs better? AVMs vs hedonic models. J. Prop. Invest. Finance 38, 3 (2020), 213--225.

[39]

Cha Zhang and Yunqian Ma. 2012. Ensemble Machine Learning: Methods and Applications. Springer.

Cited By

De Paola PPrevitera SManganelli BForte FDel Giudice F(2023)Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum ComputingBuildings10.3390/buildings1307160313:7(1603)Online publication date: 25-Jun-2023
https://doi.org/10.3390/buildings13071603

Index Terms

Towards explainable real estate valuation via evolutionary algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

Recommendations

Case-based reasoning and neural networks for real state valuation
AIAP'07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications

This paper presents an overview of two Artificial Intelligent (AI) systems, Case-Based Reasoning (CBR) and Neural Network (NN), in terms of its underlying foundation, its current state-of-the-art, and future trends in real estate industry. The use of AI ...
Real estate valuation with artificial intelligence approaches

Real Estate valuation in urban areas is a very difficult task that has absorbed the interest of many academics in the past years. Many qualitative and quantitative variables affect the value of an estate in urban areas. As a result, multivariate models ...
Case-base maintenance with multi-objective evolutionary algorithms

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

July 2022

1472 pages

ISBN:9781450392372

DOI:10.1145/3512290

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '22

Sponsor:

SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
322
Total Downloads

Downloads (Last 12 months)120
Downloads (Last 6 weeks)24

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

De Paola PPrevitera SManganelli BForte FDel Giudice F(2023)Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum ComputingBuildings10.3390/buildings1307160313:7(1603)Online publication date: 25-Jun-2023
https://doi.org/10.3390/buildings13071603

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents