It is extremely important to have a way to quickly analyze neighborhoods as a real estate investor. I am very picky when it comes to real estate investments. I am constantly on the lookout for the perfect investment opportunity. For me the perfect property has the following characteristics:
- Double-digit return potential (before adding potential appreciation)
- Located in a neighborhood that is attractive to tenants
I’ll admit that finding this ideal combination can be difficult. I could look at a hundred properties and only find one or two that actually meet this criteria. Luckily I am open to considering investments across the country. As a result, even if only 1-2% of the properties meet my standards, I have literally tens of thousands of properties to consider. All of a sudden, consistently finding that perfect property is a bit easier.
So if you’re like me and you want to earn exceptional returns without taking too much risk, you have to learn how to play the numbers game. With real estate that means creating a system that allows you to quickly and efficiently evaluate hundreds, if not thousands of properties.
There are already several simple calculations that you can use to identify higher returning properties. The most common is to take the expected monthly rent and divide it by the total acquisition price (purchase price + repairs + closing costs). If the result is higher than 1% then you have a property that will likely generate positive cash flow each month. If the result is closer to 1.5% than you have a property that may generate 20%+ returns.
Creating a System to Quickly Analyze Neighborhoods
Unfortunately there are not widely recognized tools to quickly analyze neighborhoods across the country. 15% returns sound pretty good until you find out the property is in a “war-zone”. I am starting to see more and more real estate professionals refer to neighborhoods and properties on a scale from A to F. In this system, A-grade properties are in the most attractive neighborhoods while F-grade properties sit in low-income, high-poverty neighborhoods that most investors will want to avoid.
I like the structure of this system, but it feels difficult to apply on a wide-scale. There is usually no defined standard or transparency of where the line is drawn between individual grade levels. What is a C-level neighborhood for someone might easily be a D or an F grade neighborhood to me. My sense is that most people are simply making qualitative assessments based on their local experience. That doesn’t mean that their grading is wrong, but it makes it difficult for me to compare two properties in two different neighborhoods.
So as I begin to analyze neighborhoods outside my home market, I have been compiling data to help fill this void. I have created a standardized system that I use to grade individual zip codes. In an effort to help other out-of-state investors, I have decided to provide full transparency of my neighborhood grading system. Hopefully this is a structure that other investors can utilize and even modify.
How Do You Define An Attractive Neighborhood
For me, the perfect neighborhood would have the following characteristics
- High income
- Low poverty rates
- Low unemployment
- Well educated population
- Good schools
- Low vacancy
- Stable tenants
- Reasonable distance to jobs
- Low crime
- Job growth
- Population growth
- High % of Owner-occupants
Each of these factors will help you quickly fill vacancies, reduce tenant turnover, and limit the need for evictions. With this information most people could make a reasonable assessment of a particular neighborhood. Of course this is a lot of information to pull if you’re evaluating a hundred properties in a couple dozen different areas.
So how can I make this simple and effective?
As I began to analyze neighborhoods across all of these factors I found that there were very high correlations between individual characteristics. For example, high income areas tended to have a well educated population, a high percentage of owner-occupants, and other positive factors. I ultimately identified that Median Income and Poverty Levels were the two most important drivers in predicting all of the other characteristics. It is also helpful that both of those data points are consistently available at the zip code level.
With this discovery I was able to create a simple formula for scoring each zip code
Zip Code Score = Median Zip Code Income / (Median Metro Area Rent * 40) + National Poverty Level / Zip Code Poverty Level
Ok, that doesn’t look simple, but I assure you that it’s easy to use. All you need is four data points. I will give you one of them and show you where to find the other three.
- National Poverty Level: 14.5% (from U.S. Census bureau)
- Median Metro Area Rent: Sourced from Zillow Research
- Median Zip Code Income: I source from city-data.com
- Zip Code Poverty Level: Also available on city-data.com
Note that I chose to use a nationwide benchmark for poverty while I compare income to local area rent. This way I take into account cost of living differences from one area to the next while still having some standardization across the country. Simply put a family in Houston doesn’t need to earn as much as one in New York City to be financially strong. On the other hand, families living below the poverty line are likely to be financially unstable even in lower cost markets.
Zip Code Evaluation in Action
Let’s look at a couple of examples from the metro Detroit area to see the Zip Code scores in action. The median Metro Detroit Rent is $1,171. When we multiply that times 40 we arrive at $47k. That means the average family in metro Detroit needs to earn $47k each year to comfortably afford the average rental home in the average neighborhood.
With this system, the average zip code score is 2.00. That represents a neighborhood with both average income levels (defined as 40 times the metro area rent) and average poverty levels of 14.5%.
Example Zip Code Score Comparison
Rochester Hills, MI – Zip Code: 48306
- This is one of the most attractive neighborhoods in metro Detroit
- Median Annual Income: $119k
- % Below Poverty: 2.70%
- Zip Code Score: ($119k / $47k) + (14.5% / 2.70%) = 7.90
Southfield, MI – Zip Code: 48075
- This is a nearby suburb of Detroit that has historically attracted many families that leave the city of Detroit for better schools and lower crime
- Median Annual Income: $54k
- % Below Poverty: 16.50%
- Zip Code Score: ($54k / $47k) + (14.5% / 16.50%) = 2.01
Detroit, MI – Zip Code: 48227
- This is representative of an average zip code in the city of Detroit
- Median Annual Income: $29k
- % Below Poverty: 35.80%
- Zip Code Score: ($29k / $47k) + (14.5% / 35.80%) = 1.01
Zip Code Grading Scale
So now you have a sense of how we score each zip code. Now take a look at why and how we draw the line for each grade
A – Scores above 4.00
- These zip codes are the best of the best with incomes that are roughly twice as high as the metro area average and poverty levels that are half of the national average. These areas often have the highest rent levels and most expensive property values. Zip codes with this grade will consistently attract the best possible tenants.
B – Scores from 2.85 to 3.99
- These zip codes are still well above the metro area average. These areas will often border the A-level neighborhoods and offer a more affordable option. While purchase prices will be lower in these zip codes they are still dominated by owner-occupants.
C – Scores from 1.85 to 2.84
- The majority of area zip codes will fall into this range as it represents the average income and poverty level for the area. This is often the first grade level where investors can start to consistently find properties that make good rental investment candidates.
D – Scores from 1.00 to 1.84
- These zip codes are all below average in terms of income, poverty levels or both. For most investors this will be the lowest grade level that they will want to regularly consider. Investors will need to weigh the risk of selecting from a pool of tenants that are less financially secure.
F – Scores below 1.00
- These zip codes should likely be off limits for most new investors. As income and poverty are both well below average you can expect longer vacancy periods and higher tenant turnover. The investment returns will often look quite attractive. However, you need to be particularly wary of the assumptions that are being used for these properties. You need to have significant local experience before you can reasonably estimate vacancy rates and other costs.
National Profile of Population by Zip Code Grade
I have used this system to analyze hundreds of zip codes in large metro areas all across the country. Below I have outlined the typical economic profiles of zip codes that fall within each of these grade levels.
A-Grade Zip Codes
- Median Annual Income: $80-103k
- % Below Poverty: 4-5%
- % of Renters: 14-26%
- % with Bachelor’s Degree: 36-55%
- Unemployment Rate: 5-8%
B-Grade Zip Codes
- Median Annual Income: $65-95k
- % Below Poverty: 6-7%
- % of Renters: 18-37%
- % with Bachelor’s Degree: 33-60%
- Unemployment Rate: 6-8%
C-Grade Zip Codes
- Median Annual Income: $51-66k
- % Below Poverty: 9-14%
- % of Renters: 26-42%
- % with Bachelor’s Degree: 22-40%
- Unemployment Rate: 8-11%
D-Grade Zip Codes
- Median Annual Income: $38-55k
- % Below Poverty: 15-23%
- % of Renters: 35-51%
- % with Bachelor’s Degree: 16-32%
- Unemployment Rate: 9-14%
F-Grade Zip Codes
- Median Annual Income: $30-41k
- % Below Poverty: 22-36%
- % of Renters: 48-70%
- % with Bachelor’s Degree: 9-28%
- Unemployment Rate: 9-15%
See Completed Metro Area Zip Code Analysis
I have been steadily analyzing top real estate investment markets across the country.
- Atlanta, GA
- Baltimore, MD
- Chicago, IL
- Cleveland, OH
- Dallas, TX (coming soon)
- Detroit, MI
- Houston, TX
- Los Angeles, CA (coming soon)
- Philadelphia, PA
- St. Louis, MO
I have found this zip code grading system to be a great way to quickly analyze neighborhoods all across the country. It helps to ensure I am comparing properties on an apples-to-apples basis. This system also allows me to identify opportunities where the potential investment returns exceed the normal range for a given zip code grade. Hopefully you find this helpful and can benefit from the work I’ve done. I will continue to post my analysis of various metro areas on my website. Feel free to leave a comment or send me a note if you would like me to analyze neighborhoods in a particular metro area. If I get enough requests, I’ll move it to the top of my to do list.