Representative Residential Property Model-Soft Computing Solution

Area of Science:

• Real Estate Analysis
• Soft Computing
• Data Science

Background:

• Residential properties are crucial for environment, economy, and human life quality.
• Real estate analysis faces challenges due to complex property data and evolving analytical models.
• Selecting representative data samples is vital for accurate property analysis.

Purpose of the Study:

• To assess the utility of soft computing methods for representative property model selection.
• To evaluate specific techniques like Self-Organizing Maps and Rough Set Theory.
• To improve the reality-based analysis of the residential environment.

Main Methods:

• Application of soft computing techniques, including Self-Organizing Maps (SOM) and Rough Set Theory (RST).
• Comparative analysis of these methods for their effectiveness in sample selection.
• Model development for representing the residential property environment.

Main Results:

• Soft computing methods prove useful for selecting representative property samples.
• The developed models offer a more reality-based perspective on the residential environment.
• These methods help address uncertainty and imprecision in real estate data.

Conclusions:

• Soft computing offers effective solutions for representative sample selection in real estate analysis.
• The study demonstrates the potential of SOM and RST in improving property modeling.
• Enhanced models lead to a better understanding of the complexities in the residential market.