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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65150
Volume:
10 |
6 |
Jun 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence & Machine Learning
Kandula Lakshumma Memorial College of Engineering for Women
Artificial Intelligence & Machine Learning
Kandula Lakshumma Memorial College of Engineering for Women
The rapid acceleration of urbanization alongside a surging demographic demand for residential and commercial real estate has heightened the necessity for highly precise property and construction valuation frameworks. Traditional property appraisal methodologies heavily rely on manual inspection, localized broker heuristics, and subjective expert opinions, frequently yielding inconsistent, volatile, and highly biased valuations. To mitigate these systematic tracking errors and introduce objectivity, this paper presents an integrated web-enabled Deep Learning system designed around specialized multi-layered Artificial Neural Networks (ANN). The proposed paradigm splits property valuation into a distinct dual-module architecture: a Land Price Prediction Module and a Construction Cost Prediction Module. The Land Module captures regional and environmental variations by ingesting geographical and structural descriptors including administrative states, districts, land layout classifications, plot types, and soil parameters. Concurrently, the Construction Module calculates structural structural costs by evaluating structural and physical factors such as total built-up square footage, spatial allocation metrics (BHK configuration), bathroom count, and historical asset depreciation (property age). Backed by an enterprise-grade Django framework combined with an asymmetrical SQLite database layer, the full-stack system processes relational feature matrices, encodes structural partitions via localized scaling algorithms, and projects optimized neural estimations instantly through an immersive analytical dashboard. Empirical validation proves that separating spatial land valuations from structural construction materials drastically minimizes variance spikes, providing scalable, data-driven real estate modeling that stabilizes investment risk assessment for builders, buyers, and financial underwriters alike.
KEYWORDS
This research demonstrates an operational, dual-module deep learning web platform that automates land and construction cost forecasting using Artificial Neural Networks. By moving away from manual, subjective appraisals and utilizing multi-layered feedforward networks within a responsive Django setup, the platform delivers objective, real-time real estate insight engines. The system records prediction logs, generates dashboard statistics, and connects users with regional agents to bridge the gap between AI predictions and real-world transactions. Future developments will focus on: · Integrating real-time market APIs to update model weights dynamically as economic conditions shift. · Incorporating GIS coordinates and map overlays to capture precise micro-neighborhood price changes. · Developing native mobile applications to expand accessibility for on-site field agents and buyers. · Linking the backend directly with official government property registries to ensure the highest data integrity.
1. Zhou, G., Ji, Y., Chen, X., & Zhang, F. (2018). Artificial Neural Networks and the Mass Appraisal of Real Estate. International Journal of Online and Biomedical Engineering (iJOE), 14(03), 180–187. https://doi.org/10.3991/ijoe.v14i03.8420 2. Shi, H. (2009). Determination of Real Estate Price Based on Principal Component Analysis and Artificial Neural Networks. 1, 314–317. https://doi.org/10.1109/icicta.2009.83 3. Walczak, S. (2018). Artificial Neural Networks (pp. 120–131). Igi Global. https://doi.org/10.4018/978-1-5225-2255-3.ch011 4. Walczak, S. (2019). Artificial Neural Networks (pp. 40–53). Igi Global. https://doi.org/10.4018/978-1-5225-7368-5.ch004 5. Adamu, J. (2019). Superintelligent Deep Learning Artificial Neural Networks. In Preprints.org. Mdpi Ag. https://doi.org/10.20944/preprints201912.0263.v1 6. Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Fundamentals of Artificial Neural Networks and Deep Learning (pp. 379–425). Springer. https://doi.org/10.1007/978-3-030-89010-0_10 7. Daloğlu, T., Osi, G., Aslan, M., & Beşkirli, M. (2025). Stock Price Forecasting in Finance with Artificial Neural Networks. 68–73. https://doi.org/10.1109/acie64499.2025.00018 8. Daloğlu, T., Osi, G., Aslan, M., & Beşkirli, M. (2025). Stock Price Forecasting in Finance with Artificial Neural Networks. 68–73. https://doi.org/10.1109/acie64499.2025.00018
Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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