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It’s no secret that AI has revolutionized residential and commercial real estate. From acquisitions to underwriting and property management to document management, AI has materially changed the way real estate teams operate. Property appraisals, which play a critical role in valuation and financing, are not immune from advancements in AI. While historically dominated by manual data collection and subjective judgment, real estate appraisals are also facing an AI reckoning. The result: more accurate, efficient, and transparent valuations.
The Traditional Appraisal Process
The traditional appraisal process – before the advent of AI – involves a slower and more manual experience. For example, areal estate appraiser would visit the property, collect data, examine comparable properties, and use their expertise to estimate the property’s value. This process includes considering factors such as location, size, condition, and comparable sales (or comps). Comps are recently sold properties that are similar to the one being appraised and geographically close that a real estate appraiser uses to gauge the target property’s value.
While this traditional appraisal process has its benefits, there are several inherent limitations. For example, given the subjective nature of appraisals, human bias can influence the outcome, the process can be time-consuming, and inconsistent results can be expected. Two appraisers might value the same property differently, which can lead to uncertainty and unpredictability for buyers, sellers, and investors. Is there abetter way?
How AI Can Improve Appraisals
For appraisals, AI can apply machine learning to data analysis to perform tasks that typically require human intelligence and decision-making. Since AI can process significant data faster than humans, appraisals are ripe for disruption with AI. For example, AI can process historical sales data, neighborhood trends, economic indicators, and social media sentiment. AI can also compute deal comps based on public data, which makes for a more accurate and transparent appraisal.
There are several appraisal models that can be leveraged when valuing a potential property:
Automated Valuation Models (AVMs)
An Automated Valuation Model (AVM) uses algorithms to estimate a property’s value based on a range of data inputs. These inputs can include comparable sales and property features such as square footage and the number of bedrooms and bathrooms. AVMs can quickly provide an estimated value with a high degree of accuracy, which makes AVMs popular among lenders, investors and real estate agents who want a quick and accurate valuation. However, it’s important to note that AVMs are not perfect. For example, AVMs may not be as useful when valuing unique properties where existing public data may not have relevant comps.
Machine Learning and Predictive Analytics
Beyond AVMs, AI-driven machine learning models are another tool that can improve property appraisals. In commercial real estate, AI models can continually refine their valuation estimates based on newmarket trends, sales data, and economic conditions as a few examples. Predictive analytics can also forecast future property values, which helps investors and developers make more informed decisions about their investment. By analyzing patterns and trends in historical data, AI can predict where property values are likely to go so that investors can identify emerging markets or potential risks.
Benefits of AI in Real Estate Appraisals
AI offers several benefits that improve real estate appraisals. For example:
Higher Accuracy and Consistency
AI for real estate appraisals means more accuracy. Since AI can analyze and synthesize millions of data points, AI can deliver a more consistent and reliable appraisal. This helps investors and developers make more informed decisions based on more accurate valuations.
Higher Efficiency and Speed
Traditional appraisals can take weeks to complete depending on the workload of the real estate appraiser. However, AI-powered appraisals can be completed in a fraction of the time. This time-saving is especially beneficial in high-growth markets, where timing is crucial and can mean the difference between securing a deal or missing out. Similarly, for asset managers with large real estate portfolios, the ability to leverage AI to appraise multiple properties simultaneously can yield significant time and cost savings.
Data-Driven Insights
By analyzing multiple factors that contribute to a property’s value, AI can highlight the key drivers behind an appraisal. This data-driven approach allows investors and developers to understand the rationale behind a valuation, which makes it easier to justify decisions to stakeholders.
Risk Management
AI can also play an important role in risk management. By monitoring market conditions and analyzing data, AI can identify potential risks that might not be readily apparent. For example, if AI detects lower demand for a particular type of property in a specific area, it can alert investors before the market shifts so they can adjust their strategy.
AI’s Challenges For Real Estate Appraisals
While AI offers multiple benefits, it’s not without its challenges. Data quality remains a top issue, since AI models are only as good as the data on which they are trained. If the data is incomplete, outdated, or biased, the AI model’s predictions can be flawed. If this happens, an appraisal’s valuation could be inaccurate, which could adversely impact a transaction. In addition to data quality, the lack of transparency in some AI models results in investors not understanding how an appraisal was calculated. The result can be a lack of trust in AI-generated appraisals.
Conclusion
Artificial Intelligence is transforming the real estate appraisal process. From higher accuracy and consistency to data-driven insights and risk management, AI-driven appraisals can be completed more quickly while reducing the potential for human error and bias. While challenges exist, particularly around data quality and transparency, the potential for AI to revolutionize real estate appraisals is clear.
