DECODING REAL ESTATE VALUATIONS THROUGH COMPREHENSIVE ANALYSIS OF TASHKENT’S HOUSING MARKET
Ключевые слова:
Real Estate Valuation, Exploratory Data Analysis, Outlier Detection, Elliptic Envelope Method, Mahalanobis Distance, Pearson Correlation Coefficient, Mean ImputationАннотация
This investigation delves into Tashkent city's real estate dynamics by examining a comprehensive dataset of property listings from January 2015 to September 2023, sourced from uybor.uz. Emphasizing residential real estate within the metropolitan expanse, the study rigorously preprocessed the data, ad- dressing missing values with mean imputation, outliers with the Interquartile Range (IQR) method, and standardizing numerical variables. Through Explora- tory Data Analysis (EDA), we leveraged statistical summaries and visualizations to decode distributions and inter-variable correlations, focusing on the Pearson correlation coefficient for linear relationship insights. Utilizing Elliptic Envelope Outlier Detection underpinned by the Mahalanobis distance, we isolated 186 out- liers, constituting 2.51% of the data, to uphold the integrity of our correlation assessment. The refined analysis revealed notable linear correlations between property prices and sizes, indicative of market behavior. Our methodology and analytical outcomes yield profound insights into the housing market's nuances, equipping stakeholders with data-driven guidance. The discourse on inliers and outliers underscores their influence on market analysis and valuation models, ad- vocating for nuanced interpretations that accommodate market complexity. Our findings illuminate market trends and endorse a fortified framework for real es- tate evaluation, emphasizing the critical role of outliers in understanding the full scope of market dynamics.
Библиографические ссылки
Geerts, M.; vanden Broucke, S.; DeWeerdt, J. A Survey of Methods and Input Data Types for House Price Prediction. ISPRS Int. J. Geo-Inf. 2023, 12, 200. https://doi.org/10.3390/ijgi12050200Author, F., Author, S.: Title of a proceedings paper. In:
Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016).
Dayin Li, Lianyi Liu, Haitao Lv, "Prediction of China’s Housing Price Based on a Novel Grey Seasonal Model", Mathematical Problems in Engineering, vol. 2021, Article ID 5541233, 11 pages, 2021. https://doi.org/10.1155/2021/5541233.
Housing prices in Tashkent showing downward trend since May. (2022). Kun.uz. https://kun.uz/.
Chantha Wongoutong (2022) Imputation methods for missing response values in the three parts of a central composite design with two factors, Journal of Statistical Computation and Simulation, 92:11, 2273-2289, DOI: 10.1080/00949655.2022.2027424.
Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Ashish Ghosh, An outliers detection and elimination framework in classification task of data mining, Decision Analytics Journal, Volume 6, 2023, 100164, ISSN 2772-6622, https://doi.org/10.1016/j.da- jour.2023.100164.
https://machinelearningmastery.com/
Sayyora Qulmatova, Botirjon Karimov, Munis Abdullayev, and Shirin Karimova. 2023. CROP PRODUCTION UNDER DIFFERENT CLIMATIC CONDITIONS BY ANALYZING AGRICULTURAL DATA USING MULTIPLE LINEAR REGRESSION, WINTER HOLT, AND ARTIFICIAL INTELLIGENCE. In Proceedings of the 6th International Conference on Future Networks & Distributed Systems (ICFNDS '22). Association for Computing Machinery, New York, NY, USA, 242–252. https://doi.org/10.1145/3584202.3584238
Sayyora Qulmatova, Botirjon Karimov, and Dilmurod Azimov. 2023. DATA ANALYSIS AND FORECASTING IN AGRICULTURAL ENTERPRISES. In Proceedings of the 6th International Conference on Future Networks & Distributed Systems (ICFNDS '22). Association for Computing Machinery, New York, NY, USA, 536–541. https://doi.org/10.1145/3584202.3584282.
Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. Journal of Black Psychology, 44(3), 219-246. https://doi.org/10.1177/0095798418771807.
N. N. Mohammed and A. M. Abdulazeez, "Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance," 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan, 2017, pp. 140-145, doi: 10.1109/ICIIBMS.2017.8279707.
N. N. Mohammed, M. I. Khaleel, M. Latif and Z. Khalid, "Face Recognition Based on PCA with Weighted and Normalized Mahalanobis distance," 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, Thailand, 2018, pp. 267-267, doi: 10.1109/ICIIBMS.2018.8549971.
S. Kang and S. K. Kim, "Behavior-based Outlier Detection for Indoor Environment," 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2020, pp. 734-735, doi: 10.1109/CSCI51800.2020.00135.
M. Çetiner, Ö. Dinçsoy and T. Toraman, "Outlier Detection for Analysis of Real Estate Price," 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 2020, pp. 1-4, doi: 10.1109/SIU49456.2020.9302110.
Mahsa Khoshnoud, G. Stacy Sirmans & Emily N. Zietz. (2023) The Evolution of Hedonic Pricing Models. Journal of Real Estate Literature 31:1, pages 1-47.
Nasimov, R., Nasimova, N., Botirjon, K., Abdullayev, M. (2023). Deep Learning Algorithm for Classifying Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy in Transport Workers. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2022. Lecture Notes in Computer Science, vol 13772. Springer, Cham. https://doi.org/10.1007/978-3-031-30258-9_19
Karimоv B., & Mirzaakhmedоv D. (2023). IОT BASED HОME ASSISTANT MОNITОRING RENEWABLE ENERGIES. DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 1(1), 15–30. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v1i13.
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