Residential Real Estate Office Site Selection in Maricopa County

AMY ANDERSON

University of Southern California

SSCI 581: Concepts for Spatial Thinking

December 10, 2012

Selecting a Real Estate Office Location in Maricopa County, AZ

INTRODUCTION

In 2004‐2005, the Phoenix real estate market was hot. Homes were selling via lotteries, many times before they were

officially listed for sale. In June of 2005, 74% of homes listed on the MLS sold within 30 days. Competition and demand

were sky high and fueled investor activity and led to fast‐rising prices. A “flip” economy was a “boom” for real estate

agents. In fact, by May of 2006 there were 49,626 active agent licensees in Maricopa County.

However, when the bubble burst, the Phoenix real estate market plunged from one of the best national performers to

one of the worst. Days on the market hit a high of 138 in February of 2008. Home prices plummeted, dropping by more

than half. Builders stopped projects mid‐term. Financing deals fell through. Foreclosures became commonplace and

were one of the only segments of the market performing well.

But, while the market was tanking, the southwest valley, where $3.2 billion of land was purchased by builders in 2005‐

2006, completed several development attractions. In 2006, Glendale’s University of Phoenix stadium and Westgate

Entertainment Complex opened and soon thereafter hosted the 2008 Super Bowl. Two spring training Major League

Baseball stadiums opened in 2009, bringing the west side’s stadium tally even with the east side. Five stadiums are now

on either side of the metro area. Another Super Bowl will be played in Glendale in 2015 and by then the market may be

fully rebounding.

Today, the average days on market is 68 ‐‐ down from the high of 138 and comfortably higher than the lows of 2005.

Prices have dropped considerably, but this makes Phoenix one of the most affordable markets to invest in and prices are

slowly heading upward. Builders have begun advertising again, a sign that activity is picking up.

Today, there are 36,511 active real estate licensees in Maricopa County ‐‐ a 26 percent decline since 2006. And, of that

total, less than 15 percent have a mailing address on the West side.

An analysis of spatial data confirms there is a new opportunity area and identifies several suitable locations for a new

real estate office.

OBJECTIVE

To find the best location for a new real estate office in Maricopa County, Arizona two variables were defined and

analyzed: 1) high sales potential and 2) low competition as compared to the rest of the Valley.

Data used to analyze this suitability problem included zip code level data for population growth, current existing home

listings, past year home sales, and current real estate agent locations. The data was analyzed to determine where there

is high sales activity potential and low agent competition.

AMY ANDERSON SSCI: 581 December 10, 2012

STUDY AREA

Phoenix, Arizona metropolitan area is comprised of two counties: Maricopa and Pinal. For this exercise we looked only

at Maricopa County, the more populous of the two. As of 2011, Maricopa County had 1.65 million housing units

compared to Pinal County’s 162,504 (Census).

Figure 1: Study Area, Maricopa County, AZ

Maricopa County is a large county, covering more than 9,200 square miles and encompassing rural, urban, public, and

Indian lands.

Figure 2: Urban vs. Rural distribution, as shown by population and zip code size. .

AMY ANDERSON SSCI: 581 December 10, 2012 DATA

The data used in this analysis included boundary data for counties, zip codes, and roads. The attribute data analyzed

included Census data, home sales and listings data, and real estate office and agent address information. Each of these

datasets is outlined in the chart below with source information, preparations taken to use in GIS, and which criteria the

data was used for when analyzing.

Data

Analyzed

Source Notes GIS Preparation Criteria it

supports

Population /

Housing

Units

ArcGIS Online. Source: Esri, TomTom

(using 2010 Census Data of USA Zip

Code areas used by USPS, compiled

3/19/12 with source data from

12/2011)

Added fields and calculated fields for Population

Growth 2000‐2010.

Both, used in

formulas

Home Sales

past year

Arizona Regional Multiple Listings

Service (ARMLS)

http://www.armls.com/statistics/eco

nomic‐and‐market‐watch‐reports

Original data in PDF sales by quarter and by zip

code. Manually converted to Excel datasheet,

calculated year‐to‐date and year‐over‐year

growth. Imported as geodatabase table.

Sales

Potential

Home

Listings

ARMLS via Realtor. Existing home

listings as of 10/4/2012.

26,000 address listings imported as geodatabase

table. Frequency Analysis by Zip.

Sales

Potential

Agent /

Office

Locations

Arizona Department of Real Estate as

of 10/29/12.

http://services.azre.gov/publicdataba

se/DownloadLists.aspx

36,000 address listings imported as geodatabase

table. Imported relevant fields (active residential

real estate licensees/brokers). Zip code field

clean up. Frequency Analysis by Zip Code.

Competition

In order to use the data in an apples‐to‐apples comparison I chose to use the common denominator of the datasets,

which was zip code. All the datasets provided a zip code but not all of the datasets provided a county. In order to

intersect the data with my study area I needed to append the county information to all the zip codes. To do this I

created a lookup table and eliminated zips outside the study area. The data used to do that was taken from

http://www.unitedstateszipcodes.org/zip‐code‐database/. The site claims the data comes from United States Postal

Service (2011), US Census Bureau (2010), the Internal Revenue Service (2008), and Yahoo.

In addition, much preparation was taken in the import stages as each table treated the zip code field differently. One file

had the field as a string type, while others had it as general, float, or text. These types needed to be reconciled within

the source files before importing into the geodatabase tables in order to join data correctly.

AMY ANDERSON SSCI: 581 December 10, 2012 ORIGINAL METHODOLOGY

Figure 3: Original Methodology Plan

At the start of the project assumptions were made regarding what the data might show and as the data began to reveal

information contrary to the assumptions made, the methodological flow chart began to evolve.

First, as the data was analyzed it really didn’t make sense to limit the suitability analysis to the west side, so that step

was eliminated.


Figure 4: Population Losses occurred in most of the urban center.

Second, it was assumed that population growth would exist in most zip codes. However, most of the urban zip codes

recorded populations losses from 2000‐2010. That was not expected and led the analysis to look more primarily at the

real estate specific datasets and population growth as a secondary point.

Then, with more than 36,000 real estate agents, it became cumbersome to analyze the data as pinpoint dots on the

map. The Frequency Analysis tool was discovered and used to compare zip code level statistics for home listings and

agent locations.

Lastly, although reclassifying the data would be a valid way to find a suitable location, it was determined that creating a

Boolean expression to find zip codes meeting specific criteria in each dataset would be a simpler route to the answer.

Figure 5: Geocoding Agent Address led to unwieldy analysis of dots.

AMY ANDERSON SSCI: 581 December 10, 2012 Based on those modifications the flow chart began to evolve.

Figure 6: Evolving Methodology

A cleaner look at the final methodology:

Figure 7: Final Methodology Flow Chart


Figure 9: Frequency Analysis Example

RESULTS

After determining that the real estate datasets would be most important to the analysis and understanding how to best

use them in the analysis, I began the suitability process.

First, we needed to determine which zip codes recorded increases in home sales since last year. Many zip codes have

seen declines so this narrows suitable zip codes tremendously ‐‐ only about a quarter of all study area zip codes

recorded increases in home sales since last year.

Figure 8: Home Sales Gain or Loss by Zip Jan‐Sept 2012 vs. Jan‐Sept 2011

Next, to meet the criteria of sales potential I wanted to know where homes were

currently for sale today. The source data for this was an excel sheet showing the

street address of every home for sale at the time, October 4, 2012. That data was

imported as a geodatabase table and then a frequency analysis was run to

summarize how many listings were for sale in each zip code. By running summary

statistics it was also possible to understand the average, minimum, and maxium

for sale in the zip codes. After examining the distribution of values, ranges were

developed for the graduated symbols. A high number of listings was defined to be

more than 300 listings in any one zip code.

Figure 10: Summary Statistics of Frequency Analysis of Listings


Figure 11: Agent Listings as shown by graduated symbols. More than 300 is High.

Next the same analysis was run on the excel table of agent addresses. However, in this case the data was used to

determine the relative competition in an area. To open a new office, it would be preferable to be in an area with less

competition, ie. fewer agents. So an inverse relationship was the ideal scenario ‐‐ lots of listings but few agents to

service them. Using circles to illustrate this relationship, a large pink circle with a small green circle would be key to

pinpointing these locations. The map below shows that there were in fact several zip codes fitting these requirements.

Figure 12: Looking for the inverse Relationship of Listings and Agents


Now, the last element was to go back to home sales and see if there were areas where the inverse relationship occurred

in a zip code with higher home sales this year than last year. An overlay was possible to visually see these areas (bright

lime green + large pink circle and small green circle). But, cleaner is better.

Figure 13: Zoomed in to urban area of Maricopa County shown as cluttered site suitability.

By running a Boolean operation to find areas in which the zip code met all three criteria it was possible to pinpoint

exactly which zip codes met the needs of the analysis:

Home Sales growth of 200+ homes since last year

Current Home Listings exceeding 300

Low competition: Fewer than 500 Agents

Figure 14: Suitable sites meeting all three criteria points.


Zooming in to the urban core to see these areas in relation to the freeways also provided an opportunity to include a

data table showing some of the key statistics which led to their declaration of suitability. Zip codes 85086, 85375, and

85351 all met the criteria. If we go one step further to look at the population growth statistics we threw out earlier, we

learn that two of these three also met that criteria. Both 85086 and 85375 saw population growth from 2000‐2010.

85351 saw slight declines. This could then influence the decision of where to place a new real estate office.

Figure 15: Zoomed in suitable sites showing clearer proximity to highway detail and data table of key statistics.

CONCLUSION

A future step to this project could be to locate commercial zones or parcels within each of these suitable zip codes

and/or use a commercial real estate listings site to find suitable locations that are for lease or sale.

This type of analysis could also be done in other metro markets or could be done using different criteria. For instance,

zip codes are not consistent in size (population or square miles) and so some of the data might skew towards larger zip

codes even if a smaller zip code has more potential.

When looking at historical trends related to activity in a zip code it’s also important to review zip code boundary

changes. In year 2000 there may have been fewer zip codes in Arizona and as areas became more populated those

AMY ANDERSON SSCI: 581 December 10, 2012 larger zip codes may have been split into multiple smaller zip codes. This may lead to abnormal growth or loss

percentages when comparing zip code data over time.

Also, looking just at the activity within a zip code’s boundaries may not be the most realistic. Realtors usually do

business in a region or boundary area larger than one zip code, so perhaps a driving distance buffer or another type of

analysis related to density factors would have been more appropriate.

Listings per agent could also be another element of this analysis. Rather than looking at the two items as separate

entities they are related and possibly should be shown that way more directly.

In addition, many realtors have licenses but don’t actively practice which could affect the agent density. It might be

more effective to look at realtors who have had a certain number of listings in the past year. Or maybe to look at office

locations rather than agent locations and rank them based on the number of agents per office.

There are a variety of ways this could have been approached, and as I’m new to the software I may have taken a more

simplified route than would have been taken in the real world.

REFERENCES

ArcGIS Online. Source: Esri, TomTom (using 2010 Census Data of USA Zip Code areas used by USPS, compiled 3/19/12

with source data from 12/2011)

Arizona Department of Real Estate, 2012. Download Lists. [online] Available at:

<http://services.azre.gov/publicdatabase/DownloadLists.aspx> [Accessed 5/2006 and 10/29/12]

Arizona Regional Multiple Listings Service (ARMLS), 2012. Economic and Market Watch Quarterly Reports. [online]

Available at< http://www.armls.com/statistics/economic‐and‐market‐watch‐reports> [Accessed 11/17/12]

Maricopa County ARMLS home listings as of 10/4/2012 provided by a local realtor, downloaded off of www.flexmls.com.

United States Census Bureau, State & County Quick Facts. Available at:

<http://quickfacts.census.gov/qfd/states/04/04013.html> [Accessed 10 December 2012].

UnitedStatesZipCodes.org. http://www.unitedstateszipcodes.org/zip‐code‐database/.