The Best Sources for Free Comparative Market Analysis Data

Published by
Tim Gamble
on
February 17, 2023
The Best Sources for Free Comparative Market Analysis Data

If you’re a tech savvy real estate professional, you know that performing a comparative market analysis (CMA), also referred to as a rent survey for multifamily properties, is essential for determining the value of a property. But what if you don’t have the budget to pay for expensive CMA data sources? Fear not! This blog post will explain what a CMA is, how to perform one, and will provide you with some of the best free sources of CMA data.

What is a Comparative Market Analysis?

A comparative market analysis is conducted by real estate agents or appraisers to determine the approximate value of a property based on the prices of similar properties that recently sold in the same area. A CMA usually involves comparing several factors, such as location, size, condition, and amenities. It also takes into account any recent renovations or upgrades that have been made to the property, which helps create an accurate picture of how much a potential buyer may be willing to pay for the home.

For sellers looking to get top dollar for their home, a CMA can be invaluable by providing them with an estimate of what their home is worth in today’s market. This information can help guide decisions regarding pricing and marketing strategies for selling the property. For buyers, CMAs can provide insight into whether they are getting a fair deal on a potential purchase or if they should look elsewhere for better value.

If you're ready to list your home on the market, one of the best things you can do is get a comparative market analysis from your real estate agent or appraiser before setting your price. This will give you an accurate estimate of what your home could potentially sell for and help ensure that you don't underprice or overprice it when listing it on the market. Having this information available during negotiations can also give you more bargaining power when it comes time to make an offer.

How to do a Comparative Market Analysis

Doing a CMA requires some research on your part. You'll need to first find out which properties are comparable to yours in terms of size, age, location, and features. Then, collect data on those properties—including their asking prices and sale prices—and analyze it all together to get an accurate assessment of your own property’s worth. It also helps if you can find out how long those properties have been on the market… this will help you assess whether they may be overpriced or undervalued.

If you want to accurately assess the value of a home, hiring a real estate professional to prepare a CMA report in your area may be your best option. They have access to more data points and are aware of real estate trends. However, you can still perform your own analysis even if you aren’t a realtor by using the publicly available data sources we’ll discuss below and a spreadsheet. Here are the steps to complete a CMA:

  1. Analyze the Neighborhood – Get to know the neighborhood where the property is located, including notable features like amenities, quality of local schools, attractive blocks, curb appeal, and any possible annoyances such as busy streets or active train tracks nearby.
  2. Evaluate the Subject Property – Create a detailed description of the home, listing its age, lot size, square footage, layout, style, condition, landscaping, and upgrades. Usually this is done in a side-by-side grid format, where each row has the same data point for each property.
  3. Select Your Comparable Properties – Find three to five homes that are similar to the subject property and were recently sold in the area. The best comparable properties are those that were sold within the last six months, are as close in size, style, quality, and construction type as possible, within the same school district, and ideally no more than a mile away. The accuracy of a comparative market analysis is heavily impacted by the selection of comps. Picking the wrong comps can result in overpaying as a homebuyer or leaving money on the table as a seller. Here are a few best practices for choosing comps:
  4. Discard comparable properties with sales prices that are significantly higher or lower than the norm. There is usually a reason for these variations, for example, a discounted all-cash deal or seller credit to the buyer for repairs. Including these properties in your analysis can skew results.
  5. Choose comparable properties as physically close to the subject property as possible, ideally in the same neighborhood. While an argument could be made that comps from another neighborhood with similar demographics and supply and demand characteristics may be better, appraisers don’t look at it that way (yet).
  6. Use comps with recent closing dates – sales data can change quickly in real estate markets with a lot of activity. Appraisers usually look at data from the last 6 months, but the more recent the better.
  7. Ensure that the characteristics of the comparable properties closely match those of the subject property to avoid potential errors in value adjustments. You shouldn’t compare a property built in the 60’s to a brand new development, even if they’re on the same block.
  8. Adjust for Differences Between the Comps – Compare the subject property to the comparable homes and adjust for any differences. Some appraisers use regression analysis for this, but you could just as easily provide an estimated value for certain amenities and then add or subtract that from your subject property. For example, if you determine a finished basement is worth around $25k in the market, and your comps all have finished basements but you don’t, then you’d decrease the price of your subject property by $25k to account for this.
  9. Calculate Price Per Square Foot – Once adjustments have been made, calculate each comp's price per square foot by dividing the adjusted price by its square footage. Then, take the average by adding those prices together and dividing by the number of comparables. It helps to include median and average prices for the subject property and comps (both for the property as a whole and on a per sqft basis) in your comps grid.
  10. Determine the Subject Property's Value – Finally, take the average price per square foot of the comparable properties and multiply it by the subject property's square footage to estimate its fair market value. This price may need to be adjusted depending on how similar the comparables are and other real estate market trends.

Best Sources for Free CMA Data

By taking into account factors such as size, age, location, and features as well as analyzing recent sales data from other homes in the area, high-quality CMAs allow buyers and sellers alike to make informed decisions about their real estate transactions. But where do you get the data for a CMA? See below for the top 4 recommendations for free CMA data from the team at HelloData.ai:

Zillow Zestimate Tool

The Zillow Zestimate tool isn’t just for homeowners looking to get an estimate on their own home value. It can also be used by real estate professionals as a source of CMA data. The tool provides estimated home values based on publicly available information and recent sales data. It also gives users access to historical home values, which can be useful when analyzing potential investment properties.

Realtor.com

Realtor.com is another great free resource that provides CMA data to real estate professionals. The website offers comprehensive market reports that include sales trends, median listing prices, median sale prices, and more. It also allows users to search for properties in specific areas and compare them side-by-side in order to get a better understanding of the market value of each property.

Redfin Estimate Tool

The Redfin Estimate tool is another great source for free CMA data. Like the other tools mentioned above, it provides estimated home values based on publicly available information and recent sales data. However, it also has several unique features such as a “Heat Map” that shows how much homes are selling for in various neighborhoods across the country and “Insights” that provide detailed information about local school districts and other amenities within each neighborhood.

RentSource.ai

HelloData.ai’s Rent Source API can pull real-time prices, availability & property data to your real estate technology products and market studies. With only an address, we will scour listings from several internet listing sites and query the Google Places API to help pull complete information on your subject property and comps, including data on asking rent, property value, year built, unit availability, amenities, building features, and locational amenities near your subject property. What’s more, it can pull data directly into your excel model to help you avoid copying and pasting. Whether you’re creating a CMA or multifamily rent survey, try the free demo of RentSource.ai today and accelerate your CMAs!

Finding quality CMA data doesn’t have to break your budget. With these three free sources of CMA data, you can quickly gain insights into the current real estate market without spending a dime or wasting your time researching manually. Start taking advantage of these free resources today!

Automatically Source Real Estate Data for Your Rent Surveys with the Rent Source API

The Rent Source API can add real-time rent, availability & property data to your single and multifamily CMAs. With only an address, RentSource.ai will pull data on asking rent, property value, year built, unit availability, amenities, building features, and locational amenities near your subject property from multiple internet listing sites and the Google Places API. RentSource.ai data can also be integrated directly into Excel, saving you valuable time copying and pasting. Learn more about and other HelloData.ai products from the resources below:

Co-Founder & Head of Data Engineering at HelloData. Tim is the driving force behind data structuring and engineering initiatives. His leadership role involves developing AI and computer vision-based products and overseeing the data engineering team responsible for the firm's efficient, time-saving products.

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