Geografie 2026, 131, 27-45

https://doi.org/10.37040/geografie.2026.001

Enhancing real estate decision-making: a similarity-based approach for property valuation and recommendation

Changro LeeID

Kangwon National University, Department of Real Estate, South Korea

Received June 2025
Accepted January 2026

References

1. AIZAWA, A. (2003): An information-theoretic perspective of TF-IDF measures. Information Processing & Management, 39, 1, 45−65. <https://doi.org/10.1016/S0306-4573(02)00021-3>
2. ALBONE, A. (2024): Building customer and product networks with cosine similarity in graph analytics for deep customer insight. Engineering, MAthematics and Computer Science Journal, 6, 3, 215−218. <https://doi.org/10.21512/emacsjournal.v6i3.11693>
3. BARAŃSKA, A. (2009): Qualitative and quantitative methods for assessing the similarity of real estate. Value in the process of real estate management and land administration. Towarzystwo Naukowe Nieruchomości, Olsztyn, 31−42.
4. DE RUGGIERO, M., SALVO, F. (2011): Misure di similarità negli adjustment grid methods. Aestimum, 58, 1, 47−58.
5. DEVLIN, J., CHANG, M.W., LEE, K., TOUTANOVA, K. (2019): BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
6. FAISAL, M., ZAMZAMI, E.M. (2020): Comparative analysis of inter-centroid k-means performance using Euclidean distance, Canberra distance and Manhattan distance. Journal of Physics: Conference Series, 1566, 1, pp. 012112). IOP Publishing. <https://doi.org/10.1088/1742−6596/1566/1/012112>
7. FAUZAN, R., LABIB, M.I.A., JOHANNIS, J.O.T., NOOR, S. (2022): Semantic similarity of Indonesian sentences using natural language processing and cosine similarity. In 2022 4th International Conference on Cybernetics and Intelligent System, IEEE, pp. 1−5. <https://doi.org/10.1109/ICORIS56080.2022.10031416>
8. GUNATHILAKA, T.M.A.U., MANAGE, P.D., ZHANG, J., LI, Y., KELLY, W. (2025): Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniques. Intelligent Systems with Applications, 200474. <https://doi.org/10.1016/j.iswa.2024.200474>
9. HUANG, R., CUI, C., SUN, W., TOWEY, D. (2020). Poster: Is Euclidean distance the best distance measurement for adaptive random testing? In 2020 IEEE 13th International Conference on Software Testing, Validation and VerificationIEEE, pp. 406−409. <https://doi.org/10.1109/ICST46399.2020.00049>
10. IAAO (2013): Standard on ratio studies. Kansas City, MO: International Association of Assessing Officers.
11. JOLLIFFE, I.T. (2002): Principal component analysis for special types of data, New York: Springer, 338−372.
12. KOSUB, S. (2019): A note on the triangle inequality for the Jaccard distance. Pattern Recognition Letters, 120, 36−38. <https://doi.org/10.1016/j.patrec.2018.12.007>
13. KRYSZKIEWICZ, M. (2014): The Cosine similarity in terms of the Euclidean distance. In Encyclopedia of Business Analytics and Optimization. IGI Global, 2498−2508. <https://doi.org/10.4018/978-1-4666-5202-6.ch223>
14. KUSWARDANA, D.A., PRASETYA, D.A., TRIMONO, T., DIYASA, I.G.S.M., AWANG, W.S. W. (2025): Customer transaction clustering with k-prototype algorithm using Euclidean-Hamming distance and elbow method. International Journal of Advances in Data and Information Systems, 6, 2, 259−275. <https://doi.org/10.59395/ijadis.v6i2.1381>
15. LAHITANI, A.R., PERMANASARI, A.E., SETIAWAN, N.A. (2016): Cosine similarity to determine similarity measure: Study case in online essay assessment. In 2016 4th International Conference on Cyber and IT Service Management, IEEE, 1−6. <https://doi.org/10.1109/CITSM.2016.7577578>
16. LEGENDRE, P., LEGENDRE, L. (2012): Ecological resemblance. In Developments in Environ­mental Modelling, 24, 265−335. <https://doi.org/10.1016/B978-0-444-53868-0.50007-1>
17. LESKOVEC, J., RAJARAMAN, A., ULLMAN, J.D. (2020): Mining of massive data sets. Cambridge University Press. <https://doi.org/10.1017/9781108684163>
18. LI, B., ZHOU, H., HE, J., WANG, M., YANG, Y., LI, L. (2020): On the sentence embeddings from pre-trained language models. arXiv preprint arXiv:2011.05864. <https://doi.org/10.18653/v1/2020.emnlp-main.733>
19. LI, P., YAN, H., LU, X. (2023): A Siamese neural network for learning the similarity metrics of linear features. International Journal of Geographical Information Science, 37, 3, 684−711. <https://doi.org/10.1080/13658816.2022.2143505>
20. MERIGO, J.M., CASANOVAS, M. (2011): Induced aggregation operators in the Euclidean distance and its application in financial decision making. Expert Systems With Applications, 38, 6, 7603−7608. <https://doi.org/10.1016/j.eswa.2010.12.103>
21. MIKOLOV, T., CHEN, K., CORRADO, G., DEAN, J. (2013): Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
22. NUMAN, J.A., YUSOFF, I. M. (2024): Identifying the current status of real estate appraisal methods. Real Estate Management and Valuation, 32, 4, 12−27. <https://doi.org/10.2478/remav-2024-0032>
23. PATEL, K., PATEL, H.B. (2020). A state-of-the-art survey on recommendation system and prospective extensions. Computers and Electronics in Agriculture, 178, 105779. <https://doi.org/10.1016/j.compag.2020.105779>
24. PERMATA, R.P., ALIFAH, A.N., SANJAYA, I.M.W.A. (2025): Optimizing k-means clustering through distance metric simulation for strategic enrollment segmentation in private universities. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 10, 2, 616−629. <https://doi.org/10.18860/cauchy.v10i2.33089>
25. RADOVANOVIĆ, M., NANOPOULOS, A., IVANOVIĆ, M. (2014): Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Transactions on Knowledge and Data Engineering, 27, 5, 1369−1382. <https://doi.org/10.1109/TKDE.2014.2365790>
26. RAMOS, J. (2003): Using TF-IDF to determine word relevance in document queries. In Proceedings of the First Instructional Conference on Machine Learning, 242, 1, 29−48.
27. RENIGIER-BIŁOZOR, M., JANOWSKI, A. (2024): Human-machine synergy in real estate similarity concept. Real Estate Management and Valuation, 32, 2, 13−30. <https://doi.org/10.2478/remav-2024-0010>
28. RODRAWANGPAI, B., DAUNGJAIBOON, W. (2022): Improving text classification with transformers and layer normalization. Machine Learning With Applications, 10, 100403. <https://doi.org/10.1016/j.mlwa.2022.100403>
29. SILVA, D.F., GIUSTI, R., KEOGH, E., BATISTA, G.E. (2018): Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, 32, 988−1016. <https://doi.org/10.1007/s10618-018-0557-y>
30. SINGH, R.H., MAURYA, S., TRIPATHI, T., NARULA, T., SRIVASTAV, G. (2020): Movie recommendation system using cosine similarity and KNN. International Journal of Engineering and Advanced Technology, 9, 5, 556−559. <https://doi.org/10.35940/ijeat.E9666.069520>
31. SINGH, R., SINGH, S. (2021): Text similarity measures in news articles by vector space model using NLP. Journal of The Institution of Engineers (India): Series B, 102, 329−338. <https://doi.org/10.1007/s40031-020-00501-5>
32. THRUN, M.C. (2022): Exploiting distance-based structures in data using an explainable AI for stock picking. Information, 13, 2, 51. <https://doi.org/10.3390/info13020051>
33. UYANIK, B., ORMAN, G.K. (2023): A Manhattan distance-based hybrid recommendation system. International Journal of Applied Mathematics Electronics and Computers, 11, 1, 20−29. <https://doi.org/10.18100/ijamec.1232090>
34. VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A.N., POLOSUKHIN, I. (2017): Attention is all you need. Advances in Neural Information Processing Systems, 30.
35. WANG, J., DONG, Y. (2020): Measurement of text similarity: A survey. Information, 11, 9, 421. <https://doi.org/10.3390/info11090421>
36. YANG, B., YIH, W. T., HE, X., GAO, J., DENG, L. (2014): Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575.
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ISSN 1212-0014 (Print) ISSN 2571-421X (Online)

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