Using drill downs on Big Data to help with relocation decisions

Published on September 21, 2016

A recent survey released by Apartment Guide claims that eight in ten recent college graduates would consider moving to a new city for a job, even if it wasn’t their first-choice location. There has been an upsurge in student willingness to go where the job opportunities are.

Amanda Kelly is one such young person, who has always dreamed of moving to a new city, meeting new people, making new friends, and exploring new places. Now the time has come. Newly graduated from the University of Tennessee, Knoxville where she majored in Computer Science with a concentration in Artificial Intelligence and Neural Networks, she has managed to secure three job offers:

  • The first is from a startup in financial services based out of New York City specializing in fraud detection.
  • The second company is based out of San Jose, California and is a well-funded start-up in the marketing analytics space.
  • The third company is based out of Boston, Massachusetts and specializes in biometrics.

Now Amanda wants to make an informed decision based on a large number of factors. Apart from the future of the company, the industry, current customers, future prospects etc., quality of life is very important to her. She wants to make sure she moves to a city where she can afford to live in a good place and maintain a good lifestyle.

There are two main categories of questions on her mind:

First, she will be renting a one bedroom apartment or a studio initially. Questions she wants to get answers around include:

  • What is the average rent for these properties in the three target cities?
  • How has the rent trended over the past few years?
  • What is the average cost of living in the three cities?

Second, she plans to purchase her own place in the next couple years. She would like to know:

  • Median prices of condominiums
  • Expected prices of condominiums in two years

With these questions on her mind, she reaches out to you, her real estate agent, to get professional help with making an informed decision. Understanding that your client is a tech-savvy and educated person, you pull out all the stops to create engaging and self-explanatory applications in Arcadia Instant using housing data from Zillow, an online real estate database. You want to help Amanda visualize the real estate market and cost of living index comparison between the three location so that Amanda can weigh her options.


From the app, Amanda can select a state and see a comparison of rental prices among all the cities in that state. She can also see the overall cost of living index as well as the cost index segmented by various categories like groceries, health, transportation, and utilities in all the locations. She can further select a particular city from the Rent Comparison over City Bar Chart and see how the rent has varied over the past six years.

The first thing Amanda would like to do is compare the overall cost of living between the three states – New York, California, and Massachusetts. When she hovers over each of these states one by one in the Packed Bubble Chart named State – Cost Index a tooltip shows the cost index for each state as seen below.


You explain that the average cost of living index in the US is 100. So, looking at the above visuals, it’s clear that California has the highest cost of living and Massachusetts has the lowest.

The dataset also breaks the cost of living index into average rent, groceries, health, utilities, and transportation. The following tables show these indexes for the three states.

New York


San Jose




The rents in the tables are for the cities and the cost indexes are for their respective states. The first thing Amanda notices is that the average rent is the lowest in Boston, Massachusetts. The tables also show that the groceries and transportation costs are the lowest in Massachusetts, whereas health and utilities are cheapest in New York. In your opinion, since Amanda is quite young and starting her career, she should be worrying less about the health costs and more about transportation and groceries at this point in time. Taking these factors into consideration, Boston, Massachusetts seems to promise a good lifestyle at a comparatively lower cost.

Amanda also wants to see how the average rent has trended in these cities over the past couple of years, and Arcadia Data helps you explore data to this level of granularity. When you clicks on any city on the Bar Chart, the Line Chart on the top left refreshes to show how the average rent in that city has varied over the past six years.

Clicking over New York City shows the following trend.


The trend line shows that the average rent in New York City declined after June 2014; in the past year, the rent was highest in April 2015. After June 2015, the average rent has been stable. Hovering over a point shows the average rent in a tooltip.

Similarly, in San Jose, the average rent saw a peak in August 2015 and since October 2015, the rent has been stable.


In Boston, the average rent fluctuated quite a lot over the past five years. A recent high was in November 2015, after which it declined, and has been stable for the past couple months.


This means, in all three cities, the average rent is more or less stable so the time is good to rent in any of these cities.

However, the current rent prices and cost of living are not the only factors important to Amanda. She’s a planner and intends to purchase her own condominium after a couple years. Therefore, she also wants to analyze the affordability of condominiums in these cities.

You show her another application built in Arcadia Data. It is comprised of three visuals – a Bubble Chart showing the Zillow Home Value Index (ZHVI) variation of condominium prices in these cities over the past six years, a Bar Chart showing a comparison of the current prices for condominiums in the three cities and a table showing the current condominium prices in all three cities.


Selecting a particular city refreshes the Bubble Chart to show the ZHVI variation for that particular city. You select New York City and the ZHVI variation shows the following trend.


You do an analysis to see how the condominium prices have varied from March 2014 to March 2016 to try to predict how the prices may vary from 2016 to 2018. The analysis shows that in NYC, the condominium prices increased 28.6% in the past two years. The same analysis shows that the prices in San Jose and Boston have increased 23% and 12% respectively. Assuming there’s no major fluctuation in the real estate market in the next two years and factoring in a minor fluctuation of ±0.5%, you come up with the following table showing the expected price of a condominium in the three cities.

City Current Price Expected Price @ Current Increase Rate Expected Price @ -0.5% of Current Increase Rate Expected Price @ +0.5% of Current Increase Rate
New York City 778,200 28.6% 28.1% 29.1%
1,000,765.2 996,874.2 1,004,656.2
San Jose 558,800 23% 22.5% 23.5%
687,324 684,530 690,118
Boston 499,200 12% 11.5% 12.5%
559,104 556,608 561,600


The table shows that in 2018, the expected price of a condominium will be the lowest in Boston making that the preferred choice.

Considering both the current average rent and cost of living as well as the expected price of a condominium in two years, Amanda comes to the conclusion that Boston will be a good choice for her.

With Arcadia Data, you were able to easily:

  • harness the power of data
  • perform spatial and temporal analysis on it
  • easily create interactive visualizations

This helps your client quickly find answers to the questions she had on her mind. You can access the publicly available Zillow Data here. See what insights you can find when you use Arcadia Instant for yourself.