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U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

Context

This dataset includes the median list price divided by the square footage of a 1-bedroom home for a select number of neighborhoods around the United States.

Content

When available, data includes median price per square foot on a monthly basis between January 2010 and September 2016.

Selected neighborhoods include:

• Upper East Side, New York, NY
• Spring Valley, Las Vegas, NV
• Hollywood, Los Angeles, CA
• Williamsburg, New York, NY
• Harlem, New York, NY
• Enterprise, Las Vegas,NV
• Downtown, San Jose, CA
• Sheepshead Bay, New York, NY
• Forest Hills, New York, NY
• Jackson Heights, New York, NY
• Gramercy, New York, NY
• Flagami, Miami, FL
• Downtown, Memphis, TN
• Chelsea, New York, NY
• Oak Lawn, Dallas, TX
• Greater Uptown, Houston, TX
• South Loop, Chicago, IL
• Makiki-Lower Punchbowl-Tantalus, Honolulu, HI
• Downtown, Los Angeles, CA
• Capitol Hill, Seattle, WA
• Clinton, New York, NY
• Alexandria West, Alexandria, VA
• Financial District, New York, NY
• Flatiron District, New York, NY
• Landmark-Van Dom, Alexandria, VA
• Flamingo Lummus, Miami Beach, FL
• Winchester, Las Vegas, NV
• Brickell, Miami, FL
• Waikiki, Honolulu, HI
• Back Bay, Boston, MA
• Sutton Place, New York, NY
• and several others

Inspiration

• What neighborhoods have the most expensive real estate per square foot? Least expensive?
• Which neighborhoods and/or cities have the fastest growth rates in price?
• Are there any neighborhoods that remain relatively steady in price?
• Given that this metric is listing price per square foot, is there a similar dataset that could help you compare median square footage in a 1-bedroom home across neighborhoods?

Acknowledgement

This dataset is part of Zillow Data, and the original source can be found here, under the Neighborhoods link.

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