پیش‌بینی اجاره‌بهای مسکن در مناطق 22 گانۀ شهر تهران با استفاده از الگوریتم‌های یادگیری ماشین نظارت‌شده

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری جغرافیا و برنامه‌ریزی شهری، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران

2 استاد گروه جغرافیا و برنامه‌ریزی شهری، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران

3 استاد 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

موضوعات

عنوان مقاله [English]

Predicting Residential Rents across Tehran’s 22 Districts Using Supervised Machine-Learning Algorithms

نویسندگان [English]

  • Ebrahim Rezaei 1
  • Abolfazl Meshkini 2
  • Mahdi Pourtaheri 3
1 PhD student in Geography and Urban Planning, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
2 Professor, Department of Geography and Urban Planning, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
3 Professor, Department of Geography and Rural Planning, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran.
چکیده [English]

In a dynamic real estate market, accurate residential rent prediction is important for landlords, tenants, investors, and policymakers. This study aimed to develop an effective model for predicting residential rents in Tehran using supervised machine learning algorithms. The study used a dataset of 9,891 rental properties across Tehran’s 22 districts, with 17 property-related features recorded for each observation. The algorithms were evaluated using accuracy and precision metrics. The results showed that XGBoost achieved the best predictive performance, with both accuracy and precision reaching 98.1%. The decision tree model also performed well, achieving an accuracy of 96.3% and a precision of 96.1%. The strong performance of these tree-based algorithms may be related to their ability to identify complex and nonlinear relationships in the data. The findings can help real estate professionals, investors, and policymakers make better-informed decisions and improve rental pricing and housing strategies in Tehran. Future studies could improve the predictive performance of these models by adding further variables, such as neighborhood characteristics, spatial factors, and economic indicators. The models could also be tested in other cities and regions to assess their wider use and improve understanding of rental market patterns.

کلیدواژه‌ها [English]

  • Residential rent prediction
  • supervised machine learning
  • predictive modelling
  • Tehran

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جغرافیا و آمایش شهری منطقه‌ای
دوره 16، شماره 60
مهر 1405
صفحه 63-98

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  • تاریخ دریافت: 25 خرداد 1405
  • تاریخ بازنگری: 29 تیر 1405
  • تاریخ پذیرش: 04 مرداد 1405

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