Correlation Dynamics: Charleston's Regional Price Parity and Population Growth

“If you had a problem property a year ago, you could just sell it. The tenor of multifamily has absolutely shifted to expense control and ancillary revenue, two things we didn’t talk about when rents were skyrocketing.”

- Lucas Haldeman, CEO at SmartRent

Hi Nuvo Community,

_{We pulled raw data directly from the Federal Reserve and examined Charleston's time series data on the Regional Price Parity Index (RPP) and Resident Population to uncover an interesting economic trend. The data sets, when graphed, reveal a complex interplay between economic prosperity, measured via RPP, and demographic shifts. RPP are regional price levels expressed as a percentage of the overall national price level for a given year. The price levels are determined by the average prices paid by consumers for the mix of goods and services consumed in each region. Taking the ratio of RPPs shows the difference in price levels across regions. You can see in the chart below how the 2 data sets move over time. Notice how they don’t seem to move together.}

_{Leading Indication}

_{However, it's not merely the trend lines that are noteworthy. The raw data reveals a correlation coefficient of 0.720 with a 3-year lag on the RPP data. This quantifies the degree to which the two variables move in tandem and provides a preliminary indicator for future demographic shifts. You can see the powerful correlation we found at the 3-year lag in the Raw data correlation chart below. We can examine the raw Regional Price Parity data and expect it to be a reasonable predictor of population growth in 3-years time. In simple terms, movements in the RPP today are a reasonable predictor of how the population will change three years from now.}

_{Refining the Lens: Year-over-Year Percent Change}

_{While raw data offers an initial snapshot, the year-over-year (YoY) percent changes in RPP and population provide a more nuanced understanding. This refined metric yields a stronger correlation of 0.799, observable with a 5-year lag on the RPP. The initial raw data, even when examined on a year-over-year basis, does not seem to have any real relationship, with both datasets moving in what appear to be independent trends.}

_{However, if we lag the data, hoping to tease out a delayed relationship, we find a significant correlation. That powerful 0.799 correlation coefficient occurs at a 5-year lag, as can be visualized below. The data seems to move in tandem once the lag is applied.}

_{The use of YoY changes is not merely academic; it's pragmatic. YoY metrics eliminate seasonal variations and other transient anomalies, providing a clearer signal. In data science, a correlation coefficient above 0.50 is generally regarded as strong. Thus, a coefficient of 0.799 is not just statistically significant; it's a potent predictive tool.}

_Conclusion

_{This analysis transcends mere statistical observation; it offers a quantifiable methodology for assessing and predicting demographic and economic patterns. On a 5-year lag, the YoY correlation coefficient of 0.799. The key takeaway movements in the RPP today are a reasonable predictor of how the population will change five years from now in Charleston's multifamily real estate market.}

Happy Real Estate!

All the best,

Yuri - Your Real Estate Investigator

Credit: Brian Underdahl, Chief Analytics Officer, and Jackson Burks, Economic Analyst Intern, of Nuvo Capital Partners

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