Appraising Our Future

Predictability versus Predicated; Navigating the Data

In the dynamic world of real estate, appraisers serve as the gatekeepers of property valuations, tasked with navigating the delicate balance between predictability and predicated outcomes in data analytics. As the industry increasingly relies on data-driven insights to inform decision-making, understanding the interplay between these concepts is essential for appraisers to provide accurate and reliable assessments.

Predictability in real estate data analytics refers to the degree to which historical trends and patterns can reliably forecast future market behavior. Appraisers leverage a plethora of data sources, including historical sales data, market trends, economic indicators, and demographic information, to assess the value of properties. By analyzing past performance, appraisers can identify patterns and trends that may influence future property values.

For example, appraisers may observe consistent appreciation rates in certain neighborhoods over time, allowing them to predict future price trends with a reasonable degree of confidence. Similarly, rental market data can provide insights into demand and occupancy rates, helping appraisers anticipate rental income and property cash flows.

Predicated outcomes in real estate data analytics involve making informed projections or decisions based on available data and underlying assumptions. While predictability relies on historical trends, predicated outcomes require appraisers to extrapolate from existing data to anticipate future scenarios and their potential impacts.

Appraisers may use sophisticated modeling techniques, such as regression analysis and machine learning algorithms, to predict future property values based on a combination of factors such as location, property characteristics, market conditions, and economic trends. While data and algorithms play a crucial role in prediction, human judgment remains indispensable, especially in complex and ambiguous situations. The banking industry should continue to foster a culture that values human insights, encourages critical thinking, and incorporates domain expertise into the decision-making process.

In navigating the interplay between predictability and predicated outcomes, appraisers must adopt a balanced approach that considers both historical data and forward-looking analyses. Here are some key considerations:

1. Data Quality and Accuracy: Appraisers must ensure that the data used for analysis is accurate, reliable, and up-to-date. Poor-quality data can lead to flawed predictions and unreliable predicated outcomes.

2. Market Dynamics: Real estate markets are influenced by a myriad of factors, including economic conditions, regulatory changes, and consumer preferences. Appraisers must stay abreast of market dynamics and adjust their analyses accordingly.

3. Sensitivity Analysis: Appraisers should conduct sensitivity analyses to assess the potential impact of different scenarios on property values. This involves testing various assumptions and parameters to understand the range of possible outcomes.

4. Expert Judgment: While data analytics play a crucial role in valuation, appraisers' expertise and judgment are equally important. Experienced appraisers can provide valuable insights and qualitative assessments that complement quantitative analyses.

5. Transparency and Communication: Appraisers should be transparent about their methodologies, assumptions, and limitations when communicating their findings to clients. Clear communication helps manage expectations and fosters trust in the appraisal process.

By navigating the interplay between predictability and predicated outcomes, real estate appraisers can provide accurate, reliable, and insightful valuations that enable informed decision-making in the ever-evolving real estate market.

The following example is for analysis of a neighborhood on the north side of Milwaukee.  The Statistical Market Analysis is a familiar tool provided by MLS and shows trends over a 12 month period for the neighborhood.  Obviously, we would need to analyze the data further in choosing the most relevant comparables for purposes of analysis of a particular property.

Reviewing neighborhood over a period of five years, removing outliers  

As can be seen with this example, sold prices, particularly in a period of low supply, tend to be quite volatile. The need to hire a professional who can sift through or 'clean' the data through analysis of various factors not accounted for by computer-generated models becomes apparent. Variables such as locational factors, quality of construction, differences in condition (which is a common variant as many properties are being resold by investors following remodeling), occupancy, seller motivations, and buyer motivations (e.g., low supply creating a feeding frenzy resulting in multiple offers over the asking price) can have a profound impact on pricing.

In the ever-fluctuating realm of real estate, the dynamics of supply, demand, and interest rates wield considerable influence over the market's equilibrium. Reviewing data in this same neighborhood two years ago versus today showed a stark contrast in resulting coefficients and R2 factor. Neighborhood data from 2022 resulted in an R2 factor of over 80%, while data from the same neighborhood a mere two years later resulted in an R2 factor of less than 37%. The R2 factor is a statistical measure in data modeling that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. It's a way to measure the strength of the relationship between the model's inputs and outputs. As the more recent dataset resulted in a less than satisfactory measurement, other methodologies may be needed in analyzing a particular property in relation to this neighborhood.

Supply, demand, and interest rates, the three pillars of real estate resting on the foundation of location, have created challenges in both selling real estate and property valuations.  

If you are an agent, loan officer or homeowner with questions on the appraisal process, please feel free to contact the offices of Asap Appraisals, LLC at 262-442-2323, email us at asap.appraisals@yahoo.com or visit our website at wiasapappraisals.com.