| تعداد نشریات | 28 |
| تعداد شمارهها | 869 |
| تعداد مقالات | 8,382 |
| تعداد مشاهده مقاله | 16,951,969 |
| تعداد دریافت فایل اصل مقاله | 11,350,138 |
پیشبینی اجارهبهای مسکن در مناطق 22 گانۀ شهر تهران با استفاده از الگوریتمهای یادگیری ماشین نظارتشده | ||
| جغرافیا و آمایش شهری منطقهای | ||
| مقاله 3، دوره 16، شماره 60، مهر 1405، صفحه 63-98 اصل مقاله (1.97 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22111/gaij.2026.55808.3360 | ||
| نویسندگان | ||
| ابراهیم رضائی1؛ ابوالفضل مشکینی* 2؛ مهدی پورطاهری3 | ||
| 1دانشجوی دکتری جغرافیا و برنامهریزی شهری، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2استاد گروه جغرافیا و برنامهریزی شهری، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3استاد گروه جغرافیا و برنامهریزی روستایی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران | ||
| چکیده | ||
| در چشمانداز پویای املاک و مستغلات، پیشبینی دقیق اجارۀ مسکن برای مالکان و مستأجران اهمیتی حیاتی دارد. پژوهش حاضر، بر توسعۀ مدلی کارآمد برای پیشبینی اجارۀ مسکن در شهر تهران با بهرهگیری از الگوریتمهای پیشرفتة یادگیری ماشین نظارتشده تمرکزداشته که در خلال آن، از یک مجموعهدادۀ غنی در ارتباط با املاک اجارهای در مناطق 22گانه شهر تهران(شامل ۹۸۹۱ ملک اجارهای، با ۱۷ ویژگی مرتبط با هر مشاهده)، استفادهشد. ارزیابی الگوریتمها براساس معیارهای صحت و دقت نشانداد که الگوریتم «ایکسجیبوست» به صحت و دقت قابلتوجه 1/98 درصدی دستیافت؛ درحالیکه «درخت تصمیم» صحت 3/96 درصد و دقت 1/96 درصد را کسب کرد. عملکرد قوی این الگوریتمها، از توانایی آنها در درک روابط پیچیده و غیرخطی درون دادهها و مقاومتشان در برابر بیشبرازش نشأتمیگیرد. یافتههای این مطالعه، بینشهای ارزشمندی را به متخصصان حوزة املاک، سرمایهگذاران و سیاستگذاران در تهران ارائهمیدهد تا تصمیمات کاملاً آگاهانهای اتخاذکنند و استراتژیهای بازار اجارۀ مسکن را بهبود بخشند. مسیرهای بالقوه برای پژوهشهای آینده، گنجاندن ویژگیهای بیشتری مانند مشخصات محله و شاخصهای اقتصادی را در بر میگیرد تا قابلیتهای پیشبینی الگوریتمها را بیشازپیش ارتقا دهد. علاوه بر این، بررسی قابلیت تعمیم این الگوریتمها به سایر شهرها یا مناطق میتواند راه را برای کاربردهای گستردهتر و درک جامعتر از پویایی بازار اجاره هموارسازد. | ||
| کلیدواژهها | ||
| اجارهبهای مسکن؛ یادگیری ماشین؛ مدلسازی پیشبینانه؛ تهران | ||
| مراجع | ||
|
پلتفرم آنلاین دیوار. (1402). آگهیهای اجارۀ مسکن در تهران ( منتشرشده از10/09/1402 لغایت 10/10/1402). تهران. ایران. https://divar.ir/
حسنگودرزی، سپیده و آرمانمهر، محمدرضا. (۱۳۹۷). تحلیل بازار مسکن و پیشبینی قیمت آن تا سال ۱۴۰۵؛ مطالعة موردی: شهر تهران. بررسی مسائل اقتصاد ایران، ۵(۲)، ۷۹–۱۰۳.https://economics.ihcs.ac.ir/article_4310.html?lang=en
حسینیرامندی، شیوا و کاشانی، حامد. (۱۴۰۲). مدلی ترکیبی برای تخمین قیمت مسکن؛ مطالعة موردی: شهر تهران. مهندسی سازه و ساخت، ۱۰(۱)، ۱۷۲–۱۸۹.https://doi.org/10.22065/jsce.2022.294445.2496
شهرداری تهران. (1401). آمارنامة شهرداری تهران. انتشارات سازمان فناوری اطلاعات و ارتباطات شهرداری تهران، تهران: ایران.https://data.tehran.ir/
رضائیان، سجاد؛ عسگری، حشمتالله و درویشی، باقر. (۱۳۹۸). بررسی عوامل تعیینکنندة اجاره مسکن در شهر ایلام با رویکرد اقتصادسنجی فضایی هدانیک. اقتصاد و مدیریت شهری، ۷(۲)، ۱۵–۲۷.https://iueam.ir/article-1-1189-fa.html
زالی، سعید؛ پهلوانی، پرهام و بیگدلی، بهناز. (۱۴۰۲). تحلیل فضاییـزمانی عوامل مؤثر بر قیمت مسکن؛ موردشناسی: منطقه ۵ شهرداری تهران. آمایش سرزمین، ۱۵(۱)، ۱۱۵–۱۳۰.https://doi.org/10.22059/jtcp.2022.341584.670318
زیادی، حسین؛ صلواتی، عرفان و لطفیهروی، محمدمهدی. (۱۴۰۲). پیشبینی قیمت مسکن با استفاده از الگوریتم هوش مصنوعی LSTM. تحقیقات مالی، ۲۵(۴)، ۵۵۷–۵۷۶.https://doi.org/10.22059/frj.2023.349924.1007398
صارمی، حمیدرضا؛ حیدری، محمد و آقایی، فاطمه. (۱۳۹۷). تحلیل فضایی قیمت مسکن با استفاده از تکنیک رگرسیون موزون جغرافیایی؛ مورد مطالعه: منطقه دو شهرداری تهران. اقتصاد شهری، ۳(۲)، ۱۹–۳۸.https://doi.org/10.22108/ue.2018.109447.1056
مرکز آمار ایران. (1395). نتایج سرشماری نفوس و مسکن. تهران: ایران.https://amar.org.ir/
نهاد مشترک تهیه و راهبری طرحهای توسعة شهری تهران. (1386). طرح راهبردی-ساختاری (جامع) شهر تهران. مصوب شورایعالی شهرسازی و معماری ایران در سال 1386، تهران. ایران.https://shorayeali.mrud.ir/
References
Ambrose, B. W., & Diop, M. (2018). Information Asymmetry, Regulations, and Equilibrium Outcomes: Theory and Evidence from the Housing Rental Market. Microeconomics: Asymmetric & Private Information eJournal. https://doi.org/10.2139/ssrn.2851156
Bansal, M., Goyal, A., & Choudhary, (2022). A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning. Decision Analytics Journal, 3, 100071.https://doi.org/10.1016/j.dajour.2022.00071
Baur, K., Rosenfelder, M., & Lutz, B. (2023). Automated real estate valuation with machine learning models using property descriptions. Expert Systems with Applications, 213, 119147. https://doi.org/10.1016/j.eswa.2022.119147
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54. https://link.springer.com/article/10.1007/s10462-020-09896-5
Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform (A Comprehensive Guide for Beginners) (1 ed.). Apress Berkeley, CA. https://doi.org/10.1007/978-1-4842-4470-8
Blanco, A., & Razu, D. (2019). Rental Housing. In The Wiley Blackwell Encyclopedia of Urban and Regional Studies (pp. 1-6). Wiley. https://doi.org/https://doi.org/10.1002/9781118568446.eurs0267
Bracke, P. (2013). How long do housing cycles last? A duration analysis for 19 OECD countries. Journal of Housing Economics, 22(3), 213-230. https://doi.org/10.1016/j.jhe.2013.06.001
Braubach, M., Jacobs, D. E., & Ormandy, D. (2011). Environmental burden of disease associated with inadequate housing: a method guide to the quantification of health effects of selected housing risks in the WHO European Region (World Health Organization. Regional Office for Europe, Issue. https://apps.who.int/iris/handle/10665/108587
Chen, Y., Liu, X., Li, X., Liu, Y., & Xu, X. (2016). Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning. Applied Geography, 75, 200-212. https://doi.org/10.1016/j.apgeog.2016.08.011
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 6.
Clark, S. D., & Lomax, N. (2018). A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques. Journal of Big Data, 5, Article 43. https://doi.org/10.1186/s40537-018-0154-3
Cordero, E. A., & Gutiérrez, P. R. (2021). Social Housing in Guadalajara: A Viable Strategy to Mitigate the Negative Externalities of Metropolitan Urban Sprawl? Applied Economics and Policy Studies. https://doi.org/10.1007/978-981-16-5359-9_48
David, W., Hosmer, J., Rodney, X., & Sturdivant, S. L. (2013). Applied Logistic Regression (3rd Edition ed.). Wiley & Sons. https://www.wiley.com/en-us/Applied+Logistic+Regression%2C+3rd+Edition-p-9780470582473
De Giorgio, A., Cola, G., & Wang, L. (2023). Systematic review of class imbalance problems in manufacturing. Journal of Manufacturing Systems, 71, 620-644. https://doi.org/10.1016/j.jmsy.2023.10.014
Diniz, P. S. R. (2023). Signal Processing and Machine Learning Theory (Academic Press Library in Signal Processing) (1st ed ed.). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-32-391772-8.00019-3
Elariane, S. A. (2022). Location based services APIs for measuring the attractiveness of long-term rental apartment location using machine learning model. Cities. 103588, 122. https://doi.org/10.1016/j.cities.2022.103588
Füss, R., & Koller, J. A. (2016). The role of spatial and temporal structure for residential rent predictions. International Journal of Forecasting, 32(4), 1352-1368. https://doi.org/10.1016/j.ijforecast.2016.06.001
Gao, Q., Shi, V., Pettit, C., & Han, H. (2022). Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia. Land Use Policy, 123, 106409. https://doi.org/10.1016/j.landusepol.2022.106409
Garboden, P., & Rosen, E. (2019). Serial Filing: How Landlords Use the Threat of Eviction. City & Community, 18. https://doi.org/10.1111/cico.12387
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc".
Ghaedrahmati, S., & Rezaei, E. (2024). Turkey, the second home for Iranians: push and pull motivations in Turkish housing market. Journal of European Real Estate Research, 17(1), 123-136. https://doi.org/10.1108/JERER-06-2023-0019
Gopal, Patro, K., & Sahu, K. K. (2015). Normalization: A Preprocessing Stage. ArXiv, abs/1503.06462. https://doi.org/10.48550/arXiv.1503.06462
Grybauskas, A., Pilinkienė, V., & Stundžienė, A. (2021). Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic. Journal of Big Data, 8(1), 105.https://doi.org/10.1186/s40537-021-00476-0
Gupta, S. C., & Goel, N. (2023). Predictive Modeling and Analytics for Diabetes using Hyperparameter tuned Machine Learning Techniques. Procedia Computer Science, 218, 1257-1269. https://doi.org/10.1016/j.procs.2023.01.104
Haque, M. B., Choudhury, C., & Hess, S. (2020). Understanding differences in residential location preferences between ownership and renting: A case study of London. Journal of Transport Geography, 88, 102866. https://doi.org/10.1016/j.jtrangeo.2020.102866
Hu, J., & Szymczak, S. (2023). A review on longitudinal data analysis with random forest. Brief Bioinform, 24(2). https://doi.org/10.1093/bib/bbad002
Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657-673. https://doi.org/10.1016/j.landusepمol.2018.12.030
Huang, Y. (2019). Predicting Home Value in California, United States via Machine Learning Modeling. Statistics, Optimization & Information Computing, 7. https://doi.org/10.19139/soic.v7i1.435
Iban, M. C. (2022). An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants. Habitat International, 128, 102660. https://doi.org/10.1016/j.habitatint.2022.102660
Jacobs, K., & Manzi, T. (2020). Conceptualising ‘financialisation’: governance, organisational behaviour and social interaction in UK housing. International Journal of Housing Policy, 20(2), 184-202. https://doi.org/10.1080/19491247.2018.1540737
Kang, J., Lee, H. J., Jeong, S. H., Lee, H. S., & Oh, K. J. (2020). Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence. Sustainability, 12(7), 2899. https://www.mdpi.com/2071-1050/12/7/2899
Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021). Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, 104919. https://doi.org/10.1016/j.landusepol.2020.104919
Kim, T. Y., Park, E., & Ryu, D. (2025). Determinants of housing rental prices in Seoul: Applying explainable AI. Spatial Economic Analysis, 20(2), 312–332. https://doi.org/10.1080/17421772.2024.2418906
Kishor, N. K., & Morley, J. (2015). What factors drive the price–rent ratio for the housing market? A modified present-value analysis. Journal of Economic Dynamics and Control, 58, 235-249. https://doi.org/10.1016/j.jedc.2015.06.006
Kobzan, S., Ivakhnenko, A., & Tolsta, M. (2021). Research of features of rent market development. Municipal economy of cities, 1, 116-123. https://doi.org/10.33042/2522-1809-2021-1-161-116-123
Lahmiri, S., Bekiros, S., & Avdoulas, C. (2023). A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization. Decision Analytics Journal, 6, 100166. https://doi.org/10.1016/j.dajour.2023.100166
Lee, C., & Park, K. K.-H. (2022). Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm. Engineering, Construction and Architectural Management, 29(1), 165-178. https://doi.org/10.1108/ECAM-10-2020-0850
Lenaers, I., & De Moor, L. (2023). Exploring XAI techniques for enhancing model transparency and interpretability in real estate rent prediction: A comparative study. Finance Research Letters, 58, 104306. https://doi.org/10.1016/j.frl.2023.104306
Leo Breiman, J. F., R.A. Olshen, Charles J. Stone. (2017). Classification and Regression Trees (1st ed ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315139470
Li, H. (2024). Machine Learning Methods Springer. https://link.springer.com/book/10.1007/978-981-99-3917-6
Li, R. Y. M. (2022). Housing Real Estate Economics and Finance. Journal of Risk and Financial Management, 15(3), 121. https://www.mdpi.com/1911-8074/15/3/121
Lipton, Z. C. (2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
Liu, B. (2011). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) ( 2nd ed ed.). Springer. https://link.springer.com/book/10.1007/978-3-642-19460-3
Mansour, A., Bentley, R., Baker, E., Li, A., Martino, E., Clair, A., Daniel, L., Mishra, S. R., Howard, N. J., Phibbs, P., Jacobs, D. E., Beer, A., Blakely, T., & Howden-Chapman, P. (2022). Housing and health: an updated glossary. Journal of Epidemiology and Community Health, 76(9), 833. https://doi.org/10.1136/jech-2022-219085
Meyberg, C., Rendtel, U., & Leerhoff, H. (2024). Flat rent price prediction in Berlin with web scraping. AStA Wirtschafts- und Sozialstatistisches Archiv, 18, 245–278. https://doi.org/10.1007/s11943-024-00340-6
Michael W. Berry, A. M., Bee Wah Yap. (2020). Supervised and Unsupervised Learning for Data Science (Unsupervised and Semi-Supervised Learning) ( 1st ed ed.). Springer. https://www.springerprofessional.de/en/supervised-and-unsupervised-learning-for-data-science/17138882
Mienye, D., & Sun, Y. (2022). A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. IEEE Access, PP, 1-1. https://doi.org/10.1109/ACCESS.2022.3207287
Mora-Garcia, R. T., Cespedes-Lopez, M. F., & Perez-Sanchez, V. R. (2022). Housing price prediction using machine learning algorithms in COVID-19 times. Land, 11(11), 2100. https://doi.org/10.3390/land11112100
Murtagh, F. (1991). Multilayer perceptrons for classification and regression. Neurocomputing, 2(5), 183-197. https://doi.org/10.1016/0925-2312(91)90023-5
Najib, T., Muntasir, F., & Wasi, W. A. W. (2023). Transparency in House Rent of Dhaka: Explainable AI Based Predictive Framework. https://doi.org/10.46254/BA06.20230146
Ogundunmade, T., Abidoye, M., & Olunfunbi, O. (2023). Modelling Residential Housing Rent Price Using Machine Learning Models. Modern Economy and Management, 4, 14. https://doi.org/10.53964/mem.2023014
Pilehvar, A. A., & Ghasemi, A. (2024). Advanced modeling of housing locations in the city of Tehran using machine learning and data mining techniques. Humanities and Social Sciences Communications, 11, 804. https://doi.org/10.1057/s41599-024-03244-6
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
Radhoush, S., Whitaker, B. M., & Nehrir, H. (2023). An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks. Energies, 16(16), 5972. https://www.mdpi.com/1996-1073/16/16/5972
Rafiei, M. H., & Adeli, H. (2018). Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of Construction Engineering and Management, 144. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001570
Renju, K., & Freni, S. (2024). Ensemble Approach for Predicting The Price of Residential Property. International Journal of Information Technology, Research and Applications, 3, 27-38. https://doi.org/10.59461/ijitra.v3i2.99
Rizun, N., & Baj-Rogowska, A. (2021). Can Web Search Queries Predict Prices Change on the Real Estate Market? IEEE Access, 9, 70095-70117. https://doi.org/10.1109/ACCESS.2021.3077860
Rokach, L. (2023). Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (3rd ed ed.). Springer. https://link.springer.com/book/10.1007/978-3-031-24628-9
Ryszard S, M., Jaime G, C., & Tom M, M. (2013). Machine Learning: An Artificial Intelligence Approach (1st ed ed., Vol. 1). Morgan Kaufmann. https://www.amazon.com/Machine-Learning-Artificial-Intelligence-Approach/dp/0934613095
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4), e1249. https://doi.org/10.1002/widm.1249
Sharma, H., Harsora, H., & Ogunleye, B. (2024). An Optimal House Price Prediction Algorithm: XGBoost. Analytics, 3(1), 30-45. https://www.mdpi.com/2813-2203/3/1/3
Shi, Y. (2022). Advances in Big Data Analytics: Theory, Algorithms and Practices (1st ed ed.). Springer. https://www.amazon.com/Advances-Big-Data-Analytics-Algorithms/dp/9811636060
Shuzlina, R., Nor, Z., & Sofianita, M. (2021). Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur. International Journal of Advanced Computer Science and Applications, 12. https://doi.org/10.14569/IJACSA.2021.0121291
Soltani, A., Heydari, M., Aghaei, F., & Pettit, C. (2022). Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms. Cities, 131, 103941. https://doi.org/10.1016/j.cities.2022.103941
Sujata Dash, S. K. P., Joel J. P. C. Rodrigues, Babita Majhi. (2022). Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications (Biomedical Engineering) (1st Ed ed.). CRC Press. https://doi.org/10.1201/9780367548445
Tajmiri, S., Azimi, E., Hosseini, M. R., & Azimi, Y. (2020). Evolving multilayer perceptron, and factorial design for modelling and optimization of dye decomposition by bio-synthetized nano CdS-diatomite composite. Environmental Research, 182, 108997. https://doi.org/10.1016/j.envres.2019.108997
Tang, Y., Qiu, S., & Gui, P. (2018). Predicting Housing Price Based on Ensemble Learning Algorithm. https://doi.org/10.1109/IDAP.2018.8620781
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
Teremetskyi, V., Avramova, O., Svitlychnyy, O., Sloma, V., Bodnarchuk, O., Telestakova, A., & Kokhan, V. (2021). Housing Rights Protection in the Context of Legislation and Judicial Practice of Ukraine. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4138075
Tharwat, A. (2021). Classification assessment methods. Applied computing and informatics, 17(1), 168-192.
Trinkaus, O., & Kauermann, G. (2023). Can machine learning algorithms deliver superior models for rental guides? AStA Wirtschafts- und Sozialstatistisches Archiv, 17, 305–330. https://doi.org/10.1007/s11943-023-00333-x
Truong, Q., Nguyen, M., Dang, H., & Mei, B. (2020). Housing Price Prediction via Improved Machine Learning Techniques. Procedia Computer Science, 174, 433-442. https://doi.org/10.1016/j.procs.2020.06.111
UN-Habitat. (2023). Executive Director’s Thematic Paper: Achieving the Sustainable Development Goals in times of global crises. https://unhabitat.org/executive-directors-thematic-paper-achieving-the-sustainable-development-goals-in-times-of-global
Wang, H., Yu, F., & Zhou, Y. (2018). Property Investment and Rental Rate Under Housing Price Uncertainty: A Real Options Approach (January 30, 2018). Real Estate Economics, Forthcoming, Claremont McKenna College Robert Day School of Economics and Finance Research Paper No. 3113143. https://doi.org/10.1111/1540-6229.12235
Wang, K., Zhao, H., & Li, J. (2023). Machine Learning-Based House Rent Prediction Using Stacking Integration Method. American Journal of Management Science and Engineering, 8(2), 50-55. https://doi.org/10.11648/j.ajmse.20230802.12
Wang, Y., & Zhao, Q. (2022). House Price Prediction Based on Machine Learning: A Case of King County. https://doi.org/10.2991/aebmr.k.220307.253
Watson, T. J. (2001). An empirical study of the naive Bayes classifier. (22), 41-46. https://doi.org/10.1039/b104835j
World_Bank. (2022). Poverty and shared prosperity 2022: Correcting course. World Bank. https://doi.org/https://doi.org/10.1596/978-1-4648-1893-6
Xu, K. (2022). Predicting housing prices and analyzing real estate markets in the Chicago suburbs using machine learning Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.3459
Xu, X., & Zhang, Y. (2023). A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validation. Decision Analytics Journal, 8, 100267. https://doi.org/10.1016/j.dajour.2023.100267
Xu, Y., & Goodacre, R. (2018). On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing, 2(3), 249-262. https://doi.org/10.1007/s41664-018-0068-2
Yoshida, T., Murakami, D., & Seya, H. (2024). Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset. The Journal of Real Estate Finance and Economics, 69(1), 1–28. https://doi.org/10.1007/s11146-022-09929-6
Zabor, E. C., Reddy, C. A., Tendulkar, R. D., & Patil, S. (2022). Logistic Regression in Clinical Studies. Int J Radiat Oncol Biol Phys, 112(2), 271-277. https://doi.org/10.1016/j.ijrobp.2021.08.007
Zarghamfard, M., & Meshkini, A. (2022). Analysis of factors affecting the realization of right to adequate housing in Iran: developing an interpretive-structural model. International Journal of Housing Markets and Analysis, 15(2), 411-428. https://doi.org/10.1108/IJHMA-02-2021-0021
Zhang, L. (2023). Housing Price Prediction Using Machine Learning Algorithm Journal of World Economy, 2(3), 18-26. https://www.pioneerpublisher.com/jwe/article/view/392
Zhou, J. (2023). The Private Sector in the Rental Housing Market. BCP Business & Management. https://doi.org/10.54691/bcpbm.v43i.4631 | ||
|
آمار تعداد مشاهده مقاله: 126 تعداد دریافت فایل اصل مقاله: 13 |
||