Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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