Follow Insights Blog

CoreLogic

CoreLogic Econ

LATEST CORELOGIC ECON TWEETS

Google Searches Shed Light on Rental Prices – Part II

Nationally and at the Metro Level, Google Searches Correlate with Rental Prices

Bin He    |    Housing Trends

 

In Part I of this blog we discussed how Google Trends1 can provide insights into rental prices at the national level. Here we focus on the correlation between Google Trends and real rental prices for a few large metros. As with the national level analysis, structural time series models were used to extract the long-term trends from Google search interests, and real rental prices-per-square-foot were normalized to get real rental price appreciation since January 2012.

Figure 1 DCFigure 2 DC

Figure 1 DC
Figure 2 DC


 


 

Washington D.C.

Figure 1 shows the real rental price-per-square-foot compared with Google Trends, and Figure 2 shows the real rental price appreciation since 2012 compared with long-term trends in Google search interest for Washington D.C. From January 2012 to February 2016, the long-term Google search interest has increased by 10 percent while in the same time the average real rental price has increased from $1.54/sqft to $1.665/sqft, which is an 8.1 percent appreciation. The correlation between the normalized real rental price and long-term search trends is 0.78. Figure 2 shows a similar trend at the metro level that was seen at the national level: the search interest picked up around August 2012 but the price lagged a few months and did not start to rise until the beginning of 2013.

Figure 1 DCBoston

Boston
Boston


 

Boston

Figure 3 shows the real rental price-per-square-foot compared with Google search interest, and Figure 4 shows the real rental price appreciation since Jan 2012 compared with long-term trends in Google search interest for Boston. From January 2012 to February 2016, the long-term Google search interest has increased by 15.4 percent while in the same time the average real rental price has increased from $1.91/sqft to $2.21/sqft, which is a 15.7 percent appreciation. The correlation between the real rental price appreciation and long-term search interest is as high as 0.94. From Figure 4 we can see that in Boston the Google search interest trended up until the middle of 2015 then flattened out, as did the rental price, but it lagged the Google Trends by a few months.

LALA     

LA
LA


 


 

Los Angeles

Figure 5 shows the real rental price-per-square-foot compared with Google search interest, and Figure 6 shows the real rental price appreciation since January 2012 compared with long-term trends in Google search interest for Los Angeles. From January 2012 to February 2016, the long-term Google search interest has increased by 16.2 percent while in the same time the real rental price has increased from $1.74/sqft to $2.1/sqft, which is a 20.7 percent growth. The correlation between the real rental price appreciation and long-term search interest stands at 0.992. From Figure 6 we can see that in Los Angeles the Google long-term trend was flat in 2012 and then accelerated from 2013 to the middle of 2015, and then turned flat again. The rental price displayed the same pattern, but lagged a few months.

PhiladelphiaPhiladelphia

Philadelphia
Philadelphia


 

Philadelphia

Figure 7 shows the real rental price-per-square-foot compared with Google search interest, and Figure 8 shows the real rental price appreciation compared with long-term trends in Google search interest for Philadelphia. From January 2012 to February 2016, the long-term Google search interest has increased by 4.6 percent while in the same time the average real rental price has increased from $1.01/sqft to $1.09/sqft, which is an 8 percent appreciation. The correlation between the real rental price appreciation and long-term search interest is 0.48.



1 Google Trends data measures shares of queries for a given term or several terms relative to all search terms in a geographic area. Google Trends for the category “Apartment & Residential Rentals” reflects search interest for terms including: rent, apartments for rent, houses for rent, etc.

2 0.99 is the linear correlation between these two-time series and we can see those two-time series displaying very similar non-linear patterns.

© 2016 CoreLogic, Inc. All rights reserved.