Looking at potential market via Zilow Data

[Ethan M.]

I recently did some data crunching on Zillow's public datasets. I wanted to see what markets have both a relatively high rent-to-price ratio and high appreciation. I came up with this list:

region-name	:mean-appreciation-10yr	:mean-rtp
Lakeland, FL	126.41055816	0.67416071
McAllen, TX	69.29196216	0.68613497
Memphis, TN	65.48340328	0.72944333
Dayton, OH	61.83590890	0.67836146
Pittsburgh, PA	57.91077304	0.65368893
Greensboro, NC	53.13956162	0.70635074
Youngstown, OH	51.94974231	0.65324659
Winston-Salem, NC	51.92725416	0.78515147
Rochester, NY	48.99770789	0.66227973
Augusta, GA	48.20602024	0.65745314
Toledo, OH	47.58276895	0.72120593
New York, NY	40.92053206	0.99205336
Syracuse, NY	39.81167247	0.73856038
Columbia, SC	35.11367320	0.68110439
Jackson, MS	33.01465547	0.79068896
El Paso, TX	31.68636165	0.72477202
Scranton, PA	31.55005741	0.74227059

I'm somewhat confident in the data, but not 100%. It's my first time working this data. I'm very suspicious of the numbers for New York and the appreciation for Lakeland, FL looks like a funny outlier. I don't have much actual experience with many of these markets. I'm curious what other people think of this list. Does it line up with experience? Does it look radically off? What questions do people have? 

I generated the list by combining Zillow's smooth, seasonally adjusted home value dataset (ZHVI) with their rent index (ZORI) for metro areas. Then separately I calculated the appreciation against each home value observation in the ZHVI dataset against its value 10 years earlier for each month. The numbers listed are the mean of the values for the twelve months ending this January.