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Robo-Raters: Can AI Really Judge a Home's Value?

Robo-Raters: Can AI Really Judge a Home's Value? - The Drawbacks of Traditional Appraisals

For decades, homebuyers and sellers have relied on professional appraisers to determine a property's value. While these licensed professionals aim to provide accurate and unbiased estimates, the traditional appraisal process has some inherent flaws.

One major drawback is that comparable sales data can be sparse in certain markets. Appraisers typically find recent sales of similar homes nearby to help gauge value. But in rural areas or with unique luxury properties, they may struggle to find relevant comparables. This forces more subjective adjustments by the appraiser, opening the door to human bias.

The timing of appraisals is another common pitfall. Appraisers provide a valuation at a single point in time. But housing markets are dynamic, with prices shifting daily based on supply and demand. If the appraisal comes in just before rapid price growth, it may undervalue the property. And if done just after prices peak, it could overshoot. This is frustrating for sellers hoping to capitalize on rising values.

Appraisers also have limited time and resources to assess each home. Typically they will tour the home briefly and take photographs. But they cannot scrutinize every feature or build quality. Important details like an outdated electrical system or foundation cracks may go unnoticed. This information asymmetry advantages the seller over the buyer.

Finally, traditional appraisals tend to lag behind market trends. Appraisers rely heavily on historical data from past sales. This makes them slower to account for new demand drivers, like a recent corporate relocation. Tech hubs in particular have struggled with appraisal gaps where surging prices outpace valuations.

Robo-Raters: Can AI Really Judge a Home's Value? - AI Valuation Models - How Do They Work?

With the rise of big data and advanced algorithms, a new approach to home valuations has emerged - automated appraisals by AI. These robo-raters claim to improve on the human appraisal process with lightning fast analysis and complete objectivity. But how exactly do they work?

The key advantage of AI-based models is their ability to ingest and cross-reference a huge volume of data points. For any given property, an automated valuation model (AVM) might analyze hundreds of attributes from public records, MLS listings, and other sources. This includes the obvious factors like square footage, bedroom count, age, and proximity. But it also includes granular details often overlooked by appraisers, like specific renovation permits, utility bills, traffic patterns, and microscopic neighborhood demographics.

By crunching this myriad of data, AVMs can identify similarities and differences between the subject property and tens of thousands of comparable sales. Advanced regression techniques then determine the relative contribution of each attribute to property values. For example, an extra bathroom may add 5% to a home's worth, while a new pool adds just 2%. This builds an algorithmic model of what drives prices in the hyperlocal market.

Unlike appraisers, AVMs evaluate homes on a continual basis. As new data enters the model - say a nearby sale at 10% over the expected value - the algorithm automatically adjusts its valuations accordingly. This dynamic updating enables AVM estimates to lead market trends instead of lagging behind.

The sheer computing power behind AVMs also allows for rapid-fire valuations at massive scale. While appraisers may take several days to inspect and deliver a report on one property, automated models can value hundreds of thousands of homes each day. This enables applications like real-time home price estimators on sites like Zillow and Redfin.

However, AVMs do have limitations. The GSEs still require full appraisals to corroborate AVM values above certain loan amount thresholds. And for truly one-of-a-kind luxury properties, the lack of good comps reduces algorithmic accuracy. But for the vast majority of suburban homes, early research suggests properly designed AVMs can match or even exceed the precision of human appraisers.

Robo-Raters: Can AI Really Judge a Home's Value? - What Data Do The Algorithms Use?

The data fueling AVM algorithms is far more expansive and granular than what's available to human appraisers. While appraisers may utilize dozens of data points on a given property, AVMs ingest hundreds or even thousands.

The most obvious data AVMs utilize is the standard physical characteristics of the home - square footage, bedroom and bathroom counts, lot size, age, etc. But they go much deeper, factoring in specific layouts, building materials, renovations, unique features like pools or saunas, and even the slope and solar exposure of the lot. Energy efficiency scores, utility bills, and insurance claims provide additional clues on quality and condition.

Hyperlocal market conditions are also scrutinized by layering economic indicators, demographics, transportation access, livability scores, and points of interest onto microscopic neighborhood zones. A home across the street from a brand new park will be valued differently than an identical home further away. Commute times to major employers are measured to the second.

Consumer appeal factors like school district ratings, crime rates, and registered sex offenders in the area provide insight on demand. Permits and MLS data track nearby new constructions, sales, and price trends by property type. Automated valuation models even ingest satellite imagery to monitor supply changes.

Instead of relying on a few dozen comparable sales like a human appraiser, AVMs evaluate tens of thousands of recent transactions, controlling for minute differences between properties. The most advanced systems incorporate listing prices, pending sales, and bot activity to assess real-time market momentum.

By combining public record information, crowdsourced data, and proprietary third-party data feeds, AVM algorithms create an intricately detailed portrait of property attributes, neighborhood conditions, and market dynamics relevant to each valuation.

Nevertheless, critics argue that no automated model can truly replicate the insights of an experienced local appraiser walking through a home. Important nuances like cracking foundations, faulty electrical wiring, or a problematic neighbor can be missed by even the most advanced AVM.

Defenders counter that technology is improving rapidly, allowing algorithms to account for ever more granular factors that determine value. They argue AVMs will surpass the accuracy of flawed and biased human appraisers as predictive modeling and data availability continue to advance.

Robo-Raters: Can AI Really Judge a Home's Value? - Are The Estimates Accurate?

The reliability of AVM estimates remains a sticking point for many doubtful of robo-appraisals. While AVMs boast of lightning fast valuations at massive scale, critics argue that speed and volume come at the cost of precision.

In one of the most comprehensive studies to date, researchers from the Federal Housing Finance Agency evaluated over one million appraisals and AVM estimates on homes purchased between 2013 and 2016. They found that AVMs produced valuations within 10% of the eventual sales price 75% of the time - slightly better than the 73% accuracy rate for licensed appraisers.

However, AVMs struggled more than human appraisers on lower-priced homes, with error rates nearly doubling for homes under $100,000. This suggests limitations in data availability for less expensive properties. The algorithms also fared worse for condominiums and co-ops, where individual unit attributes matter more than neighborhood factors.

Critics point out that AVMs tend to lag rapidly shifting markets, since they rely so heavily on historical sales data. This leads to systematic undervaluation in times of surging prices and overvaluation after peaks. By contrast, experienced local appraisers can better account for emerging trends in real-time.

Defenders argue that AVMs are improving quickly with advances in computing power and data feeds. Dynamic updating enables re-valuation on a daily or even hourly basis to better reflect market momentum. But even the best algorithms can be caught off guard by unexpected shocks like new zoning laws or natural disasters. Humans still hold the edge in versatility.

While AVM adoption has grown rapidly over the past decade, most lenders still require a full appraisal for high-value properties or highly unusual homes. Algorithms work best on relatively homogeneous suburban homes where massive data is available. Valuing truly one-of-a-kind luxury properties remains beyond current technological capability.

Robo-Raters: Can AI Really Judge a Home's Value? - Bias and Discrimination Concerns

A major apprehension surrounding robo-valuations is their potential to perpetuate or amplify existing societal biases. Critics argue that as algorithmic models, AVMs inherently reflect embedded prejudices in data derived from historical appraisals and sales transactions.

For example, research shows that predominantly Black neighborhoods have been consistently undervalued by human appraisers relative to comparable white areas. If an AVM is trained on such historical data, it risks propagating this racial bias into automated valuations. Experts warn that encoding past discrimination into predictive models can deepen inequality.

A recent analysis by mortgage data firm HouseCanary found that even leading AVMs exhibited systematic valuation gaps ranging from -13% to -20% in majority Black census tracts compared to those with under 6% Black population. Such algorithmic bias could further depress values and deny credit in already disadvantaged communities.

Fair housing advocates also raise concerns over the use of factors like school ratings and crime rates. While these attributes impact market values, they also closely correlate with racial demographics. Consideration of such data allows historical segregation and inequality in education and policing to influence automated valuations.

Consider an AVM factoring in school district ratings that are lower in minority areas due to decades of under-investment. The algorithm may interpret this as a property value risk and unjustly devalue homes in these neighborhoods. Critics argue such indirect dependencies propagate inequality through coded bias.

In response, AVM providers protest that eliminating factors like crime rates or school ratings would make their models less accurate to actual market dynamics. They argue that removing known value determinants leads to worse valuations, not fairer ones.

Still, a growing chorus of researchers, regulators, and housing advocates contend that AVMs must be carefully monitored for algorithmic bias and designed to promote fairness and opportunity. Some urge designers to omit sensitive variables, apply statistical corrections, or use causal modeling to avoid perpetuating historical prejudices.

Others advocate augmenting traditional property data with alternative sources indicating community potential, such as planned infrastructure projects, small business grants, and shifting citizen demographics. Testing AVM valuations against diverse human appraisers may also identify areas of bias.

Robo-Raters: Can AI Really Judge a Home's Value? - Regulating AI Appraisals

As automated valuation models proliferate, pressure is mounting for oversight and governance to ensure fairness, transparency, and accountability. Housing regulators increasingly recognize that while AI promises immense efficiency gains, its use poses novel risks that require safeguarding consumers and communities.

Several jurisdictions have already imposed disclosure requirements on AVM-based estimates. Lenders in California must inform borrowers if a computer model informed the home valuation and allow them to request an additional appraisal. And under federal law, lenders must disclose when applicants receive an automated valuation rather than a human appraisal and their right to contest the estimate.

Some fair lending advocates have called for even stricter regulations, such as requiring independent audits of valuation algorithms. They argue that absent proactive oversight, flawed or biased AVMs will produce discriminatory lending patterns that evade notice. Only active monitoring can detect masked prejudice in the black box algorithms that now determine mortgage eligibility for millions.

HUD and the FHFA are also exploring guardrails on AVMs. Rules may compel mortgage firms to demonstrate that their automated models reliably produce fair, accurate values across diverse neighborhoods. Some officials even favor requiring the inclusion of new data reflecting community socioeconomic trends and resources, not just housing characteristics.

On the industry side, the American Society of Appraisers formed a new AVM committee to draft best practices for valuing data integrity, transparency, and anti-bias controls. The Consumer Financial Protection Bureau similarly convenes stakeholders to promote ethical algorithms in lending.

But some defend the virtues of innovation and resist stifling AVMs with onerous oversight prematurely. They contend that as with all disruptive technology, early regulation risks limiting benefits to consumers. Strict controls may deprive underserved groups of expanded credit access that more liberal AVM use would provide.

Robo-Raters: Can AI Really Judge a Home's Value? - Adoption by Lenders and Buyers

On the lending side, major banks and mortgage firms now commonly use AVMs to screen applicants and supplement full appraisals. Wells Fargo, JP Morgan, and Bank of America all employ automated valuation models to assess risk, estimate loan-to-value ratios, and reduce overall appraisal costs. Government sponsored enterprises like Fannie Mae and Freddie Mac also utilize AVMs to corroborate human appraisal reports.

Industry surveys indicate over 90% of lenders now use AVMs in some capacity. Small community banks in particular have embraced robo-valuations to level the playing field against larger competitors with more in-house appraisal management. Turnaround times under 24 hours allow quicker loan processing and closing. And the lower costs of AVMs enable banks to reduce fees for borrowers.

Consumer adoption has also taken off, driven by the proliferation of instant home value estimators on sites like Zillow, Redfin, and Realtor. These have normalized the idea of algorithmic valuations for buyers and sellers. Over 90 million unique users now access AVM-powered valuations through Zillow each month. Such ubiquitous exposure has dissipated much of the skepticism surrounding robo-appraisals.

For prospective home buyers, AVMs provide preliminary valuation ballparks to anchor negotiations and set expectations. Sellers utilize them to pick opportune listing prices and determine if recent neighborhood sales warrant adjusting price targets.

However, critics argue that uneven accuracy and volatility of AVMs can mislead consumers into poor bargaining strategies or listings well below market value. They advocate verifying automated estimates against professional appraisals prior to finalizing transactions.

Proponents maintain that consumers understand the limitations of instant valuation tools and wisely incorporate them into balanced decision making. Surveys show home buyers still prioritize human appraisals over AVMs when determining purchase offers. But they increasingly reference automated guidance to orient search ranges and inform negotiations.

Robo-Raters: Can AI Really Judge a Home's Value? - The Future of Home Valuations

The future of home valuations promises to be heavily influenced by the continued advancement and adoption of AVMs. As algorithms grow more sophisticated and data availability expands, the use cases and reliability of robo-appraisals will likely increase. This could profoundly reshape valuation practices and lending norms across the housing industry.

Many experts predict that AVMs will become the primary first-line valuation tool within the next decade. Like automated credit underwriting before it, robo-appraisals appear poised to largely displace outdated manual approaches to assessing property values. Leading research firm Richey May projects that AVMs will be involved in over 75% of valuations by 2030.

Wider implementation of AVMs offers immense potential to expand credit access and affordability for underserved groups. Lower-income and minority borrowers are statistically more likely to be denied loans based on human appraisal discrepancies. Greater reliance on algorithmic valuations could reduce discrimination and enable more inclusive lending.

However, fairness advocates caution that reliance on historically biased data poses major risks of perpetuating inequality through coded prejudice. Careful AVM design and rigorous auditing will be critical to preventing automated appraisals from denying credit to already disadvantaged neighborhoods.

For buyers and sellers, near ubiquitous use of AVMs may significantly shift dynamics in real estate transactions. Easy access to algorithmic guidance could enable more informed negotiations, as buyers and sellers anchor expectations to impartial market analyses instead of agent-provided estimates. But some worry it may also contribute to overbidding and inflated listing prices if buyers lean too heavily on inflated AVM values.

While AVMs seem poised to dominate routine valuations, human appraisers retain key strengths that ensure their profession will endure. Complex custom properties, rapidly shifting markets, and dispute mediation will still require human expertise and boots on the ground. But for the vast majority of suburban single-family homes, AVM supremacy appears imminent.

As algorithms grow more accurate and trusted, lenders may come to view full appraisals as an unnecessary cost burden to cut. Valuation startup Haus promises "appraisals without appraisers" through data analytics. Such radical disintermediation alarms many in the appraisal field. But automation's advance appears inexorable.

This potential sea change in valuation practices demands thoughtful governance to protect consumers. Discrimination monitors, auditing requirements, and transparency rules will need to balance innovation against fairness and accountability. Managing societal impacts should join efficiency as a top priority.

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