Eliminating housing valuation bias



At Clear Capital, our main goal is to improve people’s lives and enable them to make accurate financial decisions about property through the use of modern analytical, technological and valuation products. We believe that property valuation plays a key role in the mortgage finance industry, as well as in the health of neighborhoods and communities. Putting people first means that anything that prevents homeowners and families from moving forward confidently is worth our time to understand and take action. This is especially true if there is potential for racial bias

When it comes to industry bias, we have an individual responsibility to resist our conscious and unconscious tendencies and determine whether we are perpetuating injustice or not. Everyone has the right to conduct their own research and self-assessment.

There are many communities that have been influenced by many years of unequal politics and continue to support concerns that these impacts exist. We must take responsibility for identifying warning signs of bias and our own blind spots.

In the midst of ongoing discussions about how to best use analytics and digitize the home screening process, we can take some clear steps to reduce bias in the mortgage industry, starting with increasing diversity in the appraiser profession.

As part of the Appraisal Diversity Initiative, the Appraisal Institute (AI) trade organization partner collaborated with Fannie Mae and the National Urban League to strengthen the Diversity Assessment initiative.

This program brings together minority communities with the appraisal profession and uses the AI ​​Education and Relief Foundation scholarships. Fannie Mae and other sponsors announced the ‘first class of 2021 aspiring real estate appraisers receiving scholarships through Estimator Diversity Initiative… “AI President Rodman Schlei noted that there have been” reports of bias in real estate appraisal, and while hard to read in the press, it has allowed the Appraisal Institute to be a leader in the field and identify any problems that may exist. “

A 2015 Harris County data study identified a risk of bias in housing valuations due to the overwhelming makeup of the industry itself and its inherent lack of diversity.

In 2020, conversations in the appraisal industry have intensified. Focusing on social upheaval and racial equality has only helped to raise awareness of the issue. Social media has amplified the experience of discrimination and touched the hearts and minds of millions. This is the first time we have tools that can objectively break bias – using structured data, machine learning, and cloud computing to test scoring models for potential bias.

Congress is considering legislation, the Fair Appraisal and Real Estate Appraisal Improvement Act 2021 (HR 2553), to create a task force to study the policy and identify any specific reasons for non-compliance, as well as to examine any barriers to entry that limit diversity in the appraiser profession. We support the intention of the law to discuss racial differences in valuation.

Evaluation bias should be considered in three forms: systemic, implicit and explicit. Systemic bias can be built into the market due to historical policy and practice, so in fact it will be deeply rooted in the data we all rely on in both automated models and human-based estimates. Implicit bias can arise if appraisers’ use of common practice further isolates neighborhoods and homeowners. AND clear bias manifests itself if the color of the skin of the property owners is guided in the assessment process.

The way forward
We must all work together to address these risks. We need to increase diversity among appraisers and industry participants – something like the National Association of Minority Mortgage Bankers of America (NAMMBA) set a goal to achieve.

First, appraisal companies need to hire more homeowner-like people. Second, developers of real estate analytics need to pay attention to the risk of systematic bias embedded in market data. Data analysis teams can improve the neutrality of their Automated Assessment Models (AVMs) and reduce the risk of “algoism”. AVMs need to be assessed to determine if they distinguish between comparable choices, data quality, and data availability. Checks and balances are important, but they must be part of a holistic approach to the accuracy of the estimate.

Finally, the verification process can be digitized to improve standardization, accuracy and objectivity in data collection. This will not only support alternative estimation methods, including hybrid estimates, but it will also reduce the likelihood of unconscious bias by improving data accuracy and reducing assumptions.


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