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Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate

    Asmae El Jaouhari Affiliation
    ; Ashutosh Samadhiya Affiliation
    ; Anil Kumar Affiliation
    ; Audrius Šešplaukis Affiliation
    ; Saulius Raslanas Affiliation

Abstract

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Keyword : real estate, automated valuation models, strategic applications, systematic literature review, PRISMA, conceptual framework

How to Cite
El Jaouhari, A., Samadhiya, A., Kumar, A., Šešplaukis, A., & Raslanas, S. (2024). Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate. International Journal of Strategic Property Management, 28(5), 286–301. https://doi.org/10.3846/ijspm.2024.22251
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Sep 30, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abidoye, R. B., & Chan, A. P. C. (2017). Artificial neural network in property valuation: Application framework and research trend. Property Management, 35(5), 554–571. https://doi.org/10.1108/PM-06-2016-0027

Abidoye, R. B., & Chan, A. P. C. (2018). Improving property valuation accuracy: A comparison of hedonic pricing model and artificial neural network. Pacific Rim Property Research Journal, 24(1), 71–83. https://doi.org/10.1080/14445921.2018.1436306

Arcuri, N., De Ruggiero, M., Salvo, F., & Zinno, R. (2020). Automated valuation methods through the cost approach in a BIM and GIS integration framework for smart city appraisals. Sustainability, 12(18), Article 7546. https://doi.org/10.3390/su12187546

Atazadeh, B., Olfat, H., Rajabifard, A., Kalantari, M., Shojaei, D., & Marjani, A. M. (2021). Linking land administration domain model and BIM environment for 3D digital cadastre in multi-storey buildings. Land Use Policy, 104, Article 105367. https://doi.org/10.1016/j.landusepol.2021.105367

Aungkulanon, P., Hirunwat, A., Atthirawong, W., Phimsing, K., Chanhom, S., & Luangpaiboon, P. (2024). Optimizing maintenance responsibility distribution in real estate management: A complexity-driven approach for sustainable efficiency. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), Article 100239. https://doi.org/10.1016/j.joitmc.2024.100239

Aydinoglu, A. C., & Sisman, S. (2024). Comparing modelling performance and evaluating differences of feature importance on defined geographical appraisal zones for mass real estate appraisal. Spatial Economic Analysis, 19(2), 225–249. https://doi.org/10.1080/17421772.2023.2242897

Batista, P., & Marques, J. L. (2021). Automated housing price valuation and spatial data. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, & C. M. Torre (Eds.), Computational science and its applications – ICCSA 2021 (pp. 366–381). Springer International Publishing. https://doi.org/10.1007/978-3-030-86973-1_26

Baur, K., Rosenfelder, M., & Lutz, B. (2023). Automated real estate valuation with machine learning models using property descriptions. Expert Systems with Applications, 213, Article 119147. https://doi.org/10.1016/j.eswa.2022.119147

Bilge, E. C., & Yaman, H. (2021). Information management roles in real estate development lifecycle: Literature review on BIM and IPD framework. Construction Innovation, 21(4), 723–742. https://doi.org/10.1108/CI-04-2019-0036

Cao, K., Diao, M., & Wu, B. (2019). A big data–based geographically weighted regression model for public housing prices: A case study in Singapore. Annals of the American Association of Geographers, 109(1), 173–186. https://doi.org/10.1080/24694452.2018.1470925

Carbonara, S., Faustoferri, M., & Stefano, D. (2021). Real Estate values and urban quality: A multiple linear regression model for defining an urban quality index. Sustainability, 13(24), Article 13635. https://doi.org/10.3390/su132413635

Cardone, B., Di Martino, F., & Senatore, S. (2024). Real estate price estimation through a fuzzy partition-driven genetic algorithm. Information Sciences, 667, Article 120442. https://doi.org/10.1016/j.ins.2024.120442

Chen, J., Wu, F., & Lu, T. (2022). The financialization of rental housing in China: A case study of the asset-light financing model of long-term apartment rental. Land Use Policy, 112, Article 105442. https://doi.org/10.1016/j.landusepol.2021.105442

Chen, N. (2022). House price prediction model of Zhaoqing city based on correlation analysis and multiple linear regression analysis. Wireless Communications and Mobile Computing, 2022(1), Article 9590704. https://doi.org/10.1155/2022/9590704

Cheng, J. C. P., Chen, W., Chen, K., & Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, Article 103087. https://doi.org/10.1016/j.autcon.2020.103087

Čirjevskis, A. (2021). Value maximizing decisions in the real estate market: Real options valuation approach. Journal of Risk and Financial Management, 14(6), Article 278. https://doi.org/10.3390/jrfm14060278

Dambon, J. A., Sigrist, F., & Furrer, R. (2021). Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction. Spatial Statistics, 41, Article 100470. https://doi.org/10.1016/j.spasta.2020.100470

Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In The Sage handbook of organizational research methods (pp. 671–689). Sage Publications Ltd.

Despotovic, M., Koch, D., Stumpe, E., Brunauer, W. A., & Zeppelzauer, M. (2023). Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning. Journal of European Real Estate Research, 16(2), 200–219. https://doi.org/10.1108/JERER-11-2022-0036

Doumpos, M., Papastamos, D., Andritsos, D., & Zopounidis, C. (2021). Developing automated valuation models for estimating property values: A comparison of global and locally weighted approaches. Annals of Operations Research, 306(1), 415–433. https://doi.org/10.1007/s10479-020-03556-1

Droj, G., Kwartnik-Pruc, A., & Droj, L. (2024). A comprehensive overview regarding the impact of GIS on property valuation. ISPRS International Journal of Geo-Information, 13(6), Article 175. https://doi.org/10.3390/ijgi13060175

El Jaouhari, A., Arif, J., Samadhiya, A., Kumar, A., & Trinkūnas, V. (2023). Are we there or do we have more to do? Metaverse in facility management and future prospects. International Journal of Strategic Property Management, 27(3), 159–175. https://doi.org/10.3846/ijspm.2023.19516

Evangelista, R., Ramalho, E. A., & Andrade e Silva, J. (2020). On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for Portugal and selected data issues. Energy Economics, 86, Article 104699. https://doi.org/10.1016/j.eneco.2020.104699

Fazeli, A., Dashti, M. S., Jalaei, F., & Khanzadi, M. (2020). An integrated BIM-based approach for cost estimation in construction projects. Engineering, Construction and Architectural Management, 28(9), 2828–2854. https://doi.org/10.1108/ECAM-01-2020-0027

Foryś, I. (2022). Machine learning in house price analysis: Regression models versus neural networks. Procedia Computer Science, 207, 435–445. https://doi.org/10.1016/j.procs.2022.09.078

Frodsham, M. (2024). Practice briefing: The implications of a move towards explicit discounted cash flow (DCF) models for property investment valuations. Journal of Property Investment & Finance, 42(4), 380–395. https://doi.org/10.1108/JPIF-04-2024-0052

Gaur, A., & Kumar, M. (2018). A systematic approach to conducting review studies: An assessment of content analysis in 25 years of IB research. Journal of World Business, 53(2), 280–289. https://doi.org/10.1016/j.jwb.2017.11.003

Ghosn, C., Warren-Myers, G., & Candido, C. (2024). Mapping the International Valuation Standards ESG criteria and sustainability rating tools adopted at scale by the Australian commercial real estate market. Journal of Property Investment & Finance, 42(5), 494–523. https://doi.org/10.1108/JPIF-03-2024-0032

Glumac, B., & Des Rosiers, F. (2020). Towards a taxonomy for real estate and land automated valuation systems. Journal of Property Investment & Finance, 39(5), 450–463. https://doi.org/10.1108/JPIF-07-2020-0087

Gröbel, S., & Thomschke, L. (2018). Hedonic pricing and the spatial structure of housing data – an application to Berlin. Journal of Property Research, 35(3), 185–208. https://doi.org/10.1080/09599916.2018.1510428

Horvath, S., Soot, M., Zaddach, S., Neuner, H., & Weitkamp, A. (2021). Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis. Land Use Policy, 107, Article 105475. https://doi.org/10.1016/j.landusepol.2021.105475

Hoxha, V. (2023). Exploring the predictive power of ANN and traditional regression models in real estate pricing: Evidence from Prishtina. Journal of Property Investment & Finance, 42(2), 134–150. https://doi.org/10.1108/JPIF-06-2023-0051

Indrajit, A., van Loenen, B., Ploeger, H., & van Oosterom, P. (2020). Developing a spatial planning information package in ISO 19152 land administration domain model. Land Use Policy, 98, Article 104111. https://doi.org/10.1016/j.landusepol.2019.104111

Jafary, P., Shojaei, D., Rajabifard, A., & Ngo, T. (2024a). Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors. Cities, 151, Article 105115. https://doi.org/10.1016/j.cities.2024.105115

Jafary, P., Shojaei, D., Rajabifard, A., & Ngo, T. (2024b). Automating property valuation at the macro scale of suburban level: A multi-step method based on spatial imputation techniques, machine learning and deep learning. Habitat International, 148, Article 103075. https://doi.org/10.1016/j.habitatint.2024.103075

Jiao, M., & Xu, H. (2022). How do collective operating construction land (COCL) transactions affect rural residents’ property income? Evidence from rural Deqing County, China. Land Use Policy, 113, Article 105897. https://doi.org/10.1016/j.landusepol.2021.105897

Kamara, A. F., Chen, E., Liu, Q., & Pan, Z. (2020). A hybrid neural network for predicting days on market a measure of liquidity in real estate industry. Knowledge-Based Systems, 208, Article 106417. https://doi.org/10.1016/j.knosys.2020.106417

Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R., & Iepsen, S. (2020). Scopus scientific mapping production in industry 4.0 (2011–2018): A bibliometric analysis. International Journal of Production Research, 58(6), 1605–1627. https://doi.org/10.1080/00207543.2019.1671625

Krämer, B., Stang, M., Doskoč, V., Schäfers, W., & Friedrich, T. (2023). Automated valuation models: Improving model performance by choosing the optimal spatial training level. Journal of Property Research, 40(4), 365–390. https://doi.org/10.1080/09599916.2023.2206823

Lee, C. L., Yam, S., Susilawati, C., & Blake, A. (2024). The future property workforce: Challenges and opportunities for property professionals in the changing landscape. Buildings, 14(1), Article 224. https://doi.org/10.3390/buildings14010224

Lee, H., Han, H., Pettit, C., Gao, Q., & Shi, V. (2024). Machine learning approach to residential valuation: A convolutional neural network model for geographic variation. The Annals of Regional Science, 72(2), 579–599. https://doi.org/10.1007/s00168-023-01212-7

Leskinen, N., Vimpari, J., & Junnila, S. (2020). Using real estate market fundamentals to determine the correct discount rate for decentralised energy investments. Sustainable Cities and Society, 53, Article 101953. https://doi.org/10.1016/j.scs.2019.101953

Li, X., Chen, J., & Ai, X. (2019). Contract design in a cross-sales supply chain with demand information asymmetry. European Journal of Operational Research, 275(3), 939–956. https://doi.org/10.1016/j.ejor.2018.12.023

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Journal of Clinical Epidemiology, 62(10), e1–e34. https://doi.org/10.1016/j.jclinepi.2009.06.006

Lisi, G. (2019). Sales comparison approach, multiple regression analysis and the implicit prices of housing. Journal of Property Research, 36(3), 272–290. https://doi.org/10.1080/09599916.2019.1651755

Liu, G. (2022). Research on prediction and analysis of real estate market based on the multiple linear regression model. Scientific Programming, 2022(1), Article 5750354. https://doi.org/10.1155/2022/5750354

Malek, J., & Desai, T. N. (2020). A systematic literature review to map literature focus of sustainable manufacturing. Journal of Cleaner Production, 256, Article 120345. https://doi.org/10.1016/j.jclepro.2020.120345

Matysiak, G. A. (2023). Assessing the accuracy of individual property values estimated by automated valuation models. Journal of Property Investment & Finance, 41(3), 279–289. https://doi.org/10.1108/JPIF-02-2023-0012

Mete, M. O., & Yomralioglu, T. (2023). A hybrid approach for mass valuation of residential properties through geographic information systems and machine learning integration. Geographical Analysis, 55(4), 535–559. https://doi.org/10.1111/gean.12350

Nor, M. I., & Raheem, M. M. (2024). Assessing the speculative dynamics and determinants of residential apartment rentals in Mogadishu, Somalia: A hybrid modeling approach. Habitat International, 144, Article 102995. https://doi.org/10.1016/j.habitatint.2023.102995

Ogunfowora, O., & Najjaran, H. (2023). Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization. Journal of Manufacturing Systems, 70, 244–263. https://doi.org/10.1016/j.jmsy.2023.07.014

Oliveira, T. C. de, Medeiros, L. de, & Detzel, D. H. M. (2021). Applying data mining algorithms to real estate appraisals: A comparative study. International Journal of Housing Markets and Analysis, 14(5), 969–986. https://doi.org/10.1108/IJHMA-07-2020-0080

Oust, A., Hansen, S. N., & Pettrem, T. R. (2020). Combining property price predictions from repeat sales and spatially enhanced hedonic regressions. The Journal of Real Estate Finance and Economics, 61(2), 183–207. https://doi.org/10.1007/s11146-019-09723-x

Özöğür Akyüz, S., Eygi Erdogan, B., Yıldız, Ö., & Karadayı Ataş, P. (2023). A novel hybrid house price prediction model. Computational Economics, 62(3), 1215–1232. https://doi.org/10.1007/s10614-022-10298-8

Pai, P.-F., & Wang, W.-C. (2020). Using machine learning models and actual transaction data for predicting real estate prices. Applied Sciences, 10(17), Article 5832. https://doi.org/10.3390/app10175832

Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50–65. https://doi.org/10.1016/j.jbusres.2022.01.027

Rampini, L., & Re Cecconi, F. (2021). Artificial intelligence algorithms to predict Italian real estate market prices. Journal of Property Investment & Finance, 40(6), 588–611. https://doi.org/10.1108/JPIF-08-2021-0073

Reite, E. J. (2023). Mortgage lending valuation bias under housing price changes and loan-to-value regulations. Finance Research Letters, 58, Article 104677. https://doi.org/10.1016/j.frl.2023.104677

Renigier-Biłozor, M., Janowski, A., & d’Amato, M. (2019). Automated valuation model based on fuzzy and rough set theory for real estate market with insufficient source data. Land Use Policy, 87, Article 104021. https://doi.org/10.1016/j.landusepol.2019.104021

Renigier-Biłozor, M., Źróbek, S., Walacik, M., Borst, R., Grover, R., & d’Amato, M. (2022). International acceptance of automated modern tools use must-have for sustainable real estate market development. Land Use Policy, 113, Article 105876. https://doi.org/10.1016/j.landusepol.2021.105876

Rey-Blanco, D., Zofío, J. L., & González-Arias, J. (2024). Improving hedonic housing price models by integrating optimal accessibility indices into regression and random forest analyses. Expert Systems with Applications, 235, Article 121059. https://doi.org/10.1016/j.eswa.2023.121059

Rosenthal, S. S., Strange, W. C., & Urrego, J. A. (2022). JUE insight: Are city centers losing their appeal? Commercial real estate, urban spatial structure, and COVID-19. Journal of Urban Economics, 127, Article 103381. https://doi.org/10.1016/j.jue.2021.103381

Saldana-Perez, M., Guzmán, G., Palma-Preciado, C., Argüelles-Cruz, A., & Moreno-Ibarra, M. (2024). Geospatial modeling of climate change indices at Mexico City using machine learning regression. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-10-2023-0153

Schirripa Spagnolo, F., Borgoni, R., Carcagnì, A., Michelangeli, A., & Salvati, N. (2024). A spatial semiparametric M-quantile regression for hedonic price modelling. AStA Advances in Statistical Analysis, 108(1), 159–183. https://doi.org/10.1007/s10182-023-00476-w

Sing, T. F., Yang, J. J., & Yu, S. M. (2022). Boosted tree ensembles for artificial intelligence based automated valuation models (AI-AVM). The Journal of Real Estate Finance and Economics, 65(4), 649–674. https://doi.org/10.1007/s11146-021-09861-1

Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38(2), 99–129. https://doi.org/10.1080/09599916.2020.1858937

Su, T., Li, H., & An, Y. (2021). A BIM and machine learning integration framework for automated property valuation. Journal of Building Engineering, 44, Article 102636. https://doi.org/10.1016/j.jobe.2021.102636

Swietek, A. R. (2024). Using automated design appraisal to model building-specific devaluation risk due to land-use change. Sustainable Cities and Society, 109, Article 105529. https://doi.org/10.1016/j.scs.2024.105529

Tajani, F., Morano, P., Salvo, F., & De Ruggiero, M. (2019). Property valuation: The market approach optimised by a weighted appraisal model. Journal of Property Investment & Finance, 38(5), 399–418. https://doi.org/10.1108/JPIF-07-2019-0094

Tanrıvermiş, H. (2020). Possible impacts of COVID-19 outbreak on real estate sector and possible changes to adopt: A situation analysis and general assessment on Turkish perspective. Journal of Urban Management, 9(3), 263–269. https://doi.org/10.1016/j.jum.2020.08.005

Tekouabou, S. C. K., Gherghina, Ş. C., Kameni, E. D., Filali, Y., & Idrissi Gartoumi, K. (2024). AI-based on machine learning methods for urban real estate prediction: A systematic survey. Archives of Computational Methods in Engineering, 31(2), 1079–1095. https://doi.org/10.1007/s11831-023-10010-5

Thomé, A. M. T., Scavarda, L. F., & Scavarda, A. J. (2016). Conducting systematic literature review in operations management. Production Planning & Control, 27(5), 408–420. https://doi.org/10.1080/09537287.2015.1129464

Trojanek, R., Gluszak, M., & Trojanek, M. (2024). Public land leases, reforms and (in)stability of municipal revenues in Poland – The case of Poznan city. Cities, 148, Article 104877. https://doi.org/10.1016/j.cities.2024.104877

Valdez Gómez de la Torre, F. M., & Chen, X. (2024). Housing price determinants in Ecuador: A spatial hedonic analysis. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-09-2023-0121

Vieira, E., & Gomes, J. (2009). A comparison of Scopus and Web of Science for a typical university. Scientometrics, 81(2), 587–600. https://doi.org/10.1007/s11192-009-2178-0

Wan, W. X., & Lindenthal, T. (2023). Testing machine learning systems in real estate. Real Estate Economics, 51(3), 754–778. https://doi.org/10.1111/1540-6229.12416

Wang, D., & Li, V. J. (2019). Mass appraisal models of real estate in the 21st Century: A systematic literature review. Sustainability, 11(24), Article 7006. https://doi.org/10.3390/su11247006

Wang, R., & Rasouli, S. (2022). Contribution of streetscape features to the hedonic pricing model using geographically weighted regression: Evidence from Amsterdam. Tourism Management, 91, Article 104523. https://doi.org/10.1016/j.tourman.2022.104523

Wang, Y., Wang, S., Li, G., Zhang, H., Jin, L., Su, Y., & Wu, K. (2017). Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 79, 26–36. https://doi.org/10.1016/j.apgeog.2016.12.003

Wei, C., Fu, M., Wang, L., Yang, H., Tang, F., & Xiong, Y. (2022). The research development of hedonic price model-based real estate appraisal in the era of big data. Land, 11(3), Article 334. https://doi.org/10.3390/land11030334

Xia, H., Liu, Z., Efremochkina, M., Liu, X., & Lin, C. (2022). Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration. Sustainable Cities and Society, 84, Article 104009. https://doi.org/10.1016/j.scs.2022.104009

Yalpir, S., Sisman, S., Akar, A. U., & Unel, F. B. (2021). Feature selection applications and model validation for mass real estate valuation systems. Land Use Policy, 108, Article 105539. https://doi.org/10.1016/j.landusepol.2021.105539

Yasnitsky, L. N., Yasnitsky, V. L., & Alekseev, A. O. (2021). The complex neural network model for mass appraisal and scenario forecasting of the urban real estate market value that adapts itself to space and time. Complexity, 2021(1), Article 5392170. https://doi.org/10.1155/2021/5392170

Zaki, J., Nayyar, A., Dalal, S., & Ali, Z. H. (2022). House price prediction using hedonic pricing model and machine learning techniques. Concurrency and Computation: Practice and Experience, 34(27), Article e7342. https://doi.org/10.1002/cpe.7342

Zhang, X., Ma, Y., & Wang, M. (2024). An attention-based Logistic-CNN-BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises. Expert Systems, 41(2), Article e13299. https://doi.org/10.1111/exsy.13299

Zhang, Y., Xian, J., & Huang, M. (2020). Online leasing strategy for depreciable equipment considering opportunity cost. Information Processing Letters, 162, Article 105981. https://doi.org/10.1016/j.ipl.2020.105981

Zhou, Q., Shao, Q., Zhang, X., & Chen, J. (2020). Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density. Land Use Policy, 91, Article 104410. https://doi.org/10.1016/j.landusepol.2019.104410