PROPERTY VALUATIONS AND AI THROUGH AUTOMATED VALUATION MODELS (AVMs)
1.- Accuracy and Efficiency
AI algorithms use historical transaction data, property features, neighborhood characteristics and market trends to enhance valuation accuracy reducing time and potential human error.
2.- Real Time Adjustments
AI can update valuations in real-time, reflecting current market conditions more dynamically than traditional methods, which often rely on outdated data.
3.- Bias Reduction
AI can reduce human biases in valuations. While AVMs might be less discriminatory than human appraisers in some aspects, they can still reflect biases present in the data they’re trained on, like overvaluing properties in certain neighborhoods.
4.- Transparency and Trust
The integration of AI into property valuation has raised concerns about transparency. There’s a growing demand for AI developers to disclose how valuations are calculated, including data sources and algorithms used, to build trust and ensure fairness.
5.- Challenges
The effectiveness of AI in valuations depends on the quality of data. Incomplete or baised data can lead to inaccurate valuations. AI models might not capture unique property features well, which human appraisers might notice.
6.- Regulatory and Market Response
Regulatory bodies are recognizing AVMs with adjustments in policies to allow for their use in mortgage processes, indicating a move towards AI-driven valuation acceptance.
Author: D.B.A. Jaime B
