Methodology

The usual suspects

Numerous websites offer pre-populated lists of "Top 25 cities for..." either based on arbitrary criteria or poorly functioning filters. Pre-populated lists lack personalized input, instead simply reflecting the author's preferences.

The second type is an actively filtered list. While these are less frequently encountered, it's commendable that they attempt to incorporate user preferences. At first glance, the filtering option seems promising. You click through some filters and select moderate cost of living, warm weather, population density not too high, housing prices at median or less. Suddenly you have only three results, all in rural Mississippi. While rural Mississippi is fine, it is far from what I was looking for. What went wrong? This example underscores the inherent limitation in the filtering methodology: it can inadvertently narrow down options excessively. Just because someone prefers warmth doesn't mean they want to eliminate all places with a chilly February.

How do we do this better?

Our approach is fundamentally opposite from the filtering method. Rather than excluding options through filtering, we systematically evaluate and score each option based on your prioritized criteria.

Everyone has a unique set of factors they prioritize, whether they are purchasing, renting, or investing in real estate. For instance, if you value a pleasant climate, select the MoveorInvest.com Climate Grade factor, and MSAs with better climates will gain higher scores. However, colder climate areas aren't disregarded as they might excel in other aspects you deem important. After weighing all your priorities, you may be surprised by which MSAs emerge as top contenders.

So really, which markets are best?

Your ideal location hinges on various factors tailored to your preferences. While some prioritize favorable weather, others may be looking for lower cost of living. Investors may target cash flow, appreciation, low unemployment rates or all of the above. Our platform empowers YOU to curate rankings based solely on the factors that resonate with your needs, ensuring the places are perfectly matched to you.

Scoring mechanism

Our scoring for each of the various factors is accomplished by setting the ideal characteristic or criteria for each factor and comparing the performance of all MSAs against that ideal value. For some factors, that means simply scoring each MSA value against the best value in the dataset to produce a scaled score, with the best MSA receiving a 10/10. For other factors, where the range of absolute values is a tight distribution, we determine the standard deviation and score based on how many standard deviations the target value is away from the optimum value.

The result is a score for each factor that is comparative in nature, making it useful for seeing the difference between each of the MSAs. Weighting is applied to gauge each factor's influence on the total score, differing for personal moves versus investment purposes. Finally, the total score for each MSA is ranked to produce your custom list.

The data

If you are reading this section, I commend you. I too like to know how the underlying data is used to build a list. It doesn’t matter how pretty the presentation is if the underlying data is junk. We only utilize current data to ensure reliable scoring and rankings. Unlike some platforms employing outdated data or scoring despite data gaps, our database is updated and comprehensive, eliminating the risk of making an important decision based on poor information.

Sources

• U.S. Census – 2020
• U.S. Census – 2010
• U.S. Bureau of Labor Statistics
• Federal Bureau of Investigations (FBI)
• National Oceanic and Atmospheric Administration (NOAA)
• Environmental Protection Agency (EPA)
• Federal Emergency Management Agency (FEMA)
• National Center for Education Statistics
• National Assessment of Educational Progress (NAEP)
• National Association of Realtors
• Zillow
• Trulia
• Numbeo
• Wallethub