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Data & Analytics

Dissecting the Data

What a Year’s Worth of Investor Input Tells Us About the Future by Mitchell Zagrodnik RCN Capital and CJ Patrick Company’s Investor Sentiment Survey, which launched initially in Spring 2023, gauges real estate investors’ views on market conditions. The survey features a static set of questions that allow data to be compared and analyzed for trends over time. Periodically, more topical questions will also be included in the survey. With this being an election year, the Fall 2024 iteration included questions about the presidential candidates, and how investors see the market performing depending on which nominee is victorious. The Fall 2024 Survey results showed that fix-and-flip investors had an overall more positive outlook about market conditions than long-term rental investors. Where 80% of flippers believe that market conditions have improved over the past year, only 47% of rental property investors believe that today’s market is better than last year’s. As we dive into the overall sentiment from the participants in the most recent survey, the contrast between flippers and rental investors is a notable theme throughout this piece. And now with over a year’s worth of data from these reports, we can compare survey responses year-over-year. Overall Investor Sentiment As of Fall 2024, investor optimism was the highest recorded in the six quarters of the survey’s existence. When asked if market conditions are better today than they were a year ago, 68% of participants responded yes, and over 71% believed that conditions would continue to improve in the following months. Declining financing costs, increased inventory, and a gradual slowdown in price appreciation are contributing factors to that optimism. The three biggest challenges facing real estate investors are listed below, and we were able to compare the responses to the same question during the Spring 2023 survey:  »            High Cost of Financing // 62.88% in Fall 2024 compared to 72.70% in Spring 2023  »            Competition from Institutional Investors // 43.56% in Fall 2024 compared to 33.88% in Spring 2023  »            Lack of Inventory // 39.57% in Fall 2024 compared to 47.70% in Spring of 2023 The shift towards a more optimistic outlook for the real estate investment space is increasingly apparent after seeing this year-over-year change in responses. A majority of real estate investors utilize some form of financing in order to secure properties, so the nearly 10% drop in the responses year-over-year is telling. It is also notable that concern over a lack of inventory has dropped by about 8% year-over-year. Low housing inventory has been a consistent obstacle facing homebuyers over the past several years. This issue is being addressed with an increased number of new residential construction projects, specifically single-family homes, hitting the market. According to data provided by the Department of Housing and Urban Development in September 2024, the total recorded house completions in August 2024 was 1,788,000. This is the highest number on record in the month of August within the last five years. These numbers should give people confidence that this problem is being addressed. Insurance Costs Proving to be an Ongoing Issue  Insurance is a crucial factor in the homebuying process, and lately this necessary step has become a consistent deal killer. When asked if rising insurance costs or the inability to insure properties factored into the decision to invest in real estate, 80% of survey participants in the Fall 2024 iteration responded “yes.” For fix-and-flip investors, 82.9% felt that cost and availability of insurance was a deciding factor in their real estate investments, whereas only 69.4% of rental investors felt that way. Based on the responses provided by the survey, insurance issues have caused more flippers to miss out on a deal than rental investors by a difference of 73.3% for flippers versus 45% for rental investors. That stark contrast emphasizes the problems that flippers are facing when it comes to insurance, and these issues appear to be even more apparent in certain areas of the country. The issue of insurance coverage is especially prevalent in states that are susceptible to extreme weather events, with California and Florida garnering the most attention. Florida has recently experienced devastating hurricanes, which in general, are unfortunately common in the southern region of the United States. California has been susceptible to large-scale wildfires. These natural events have caused insurance rates to skyrocket, and even some insurance companies to leave these states entirely. In California, 97% of investors have experienced issues with insurance cost and availability, and in Florida that number is at 93%. The breakdown of each state based on investment strategy is below: California-based Investors’ likelihood of missing out on a deal due to insurance:  »            Fix-and-Flip: 87.5%  »            Rental Investors: 50% Florida-based Investors’ likelihood of missing out on a deal due to insurance:  »            Fix-and-Flip: 60%  »            Rental Investors: 60% The breakdown shows it has been more of an issue for flippers compared to rental investors in California, but in Florida the numbers are relatively similar by investmenttype. It will be fascinating to see if this continues to be a problem over the next 12 months. Presidential Election Factors Of the participants in the Fall 2024 survey, 51.4% are backing Kamala Harris versus 40.5% for Donald Trump. Harris is also seen as the candidate who will lead to a better investment environment. 47.22% believe that, while 39.20% see a Trump presidency as more beneficial to investors. What is interesting is how flippers and rental investors differ in which candidate will lead to a better investing environment. Fix-and-flip investors lean towards Harris with 56.9% believing she will create a better investment market, whereas 32.6% favor Trump in that regard. The opposite is the case for rental investors. 45% of the respondents believe Trump will create a more favorable investing environment, versus 39.6% believing Harris will. Some of the major talking points of the Harris campaign are policies aimed directly at long-term rental investors, like the controversial topic of rent control. Based on that, it makes sense for investors that primarily own rentals to favor Trump.

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Real Estate Meets AI

Productivity Gains and Persistent Limitations by Jonas Bordo In the fall of 2022, OpenAI released ChatGPT and made the power and promise of artificial intelligence (AI) a tangible reality. To date, the impact has been most profound in the area of human productivity. For example, here at Dwellsy, we precisely map every SFR and apartment address that comes into our system (hundreds of thousands per month), and what was once a labor-intensive process is dramatically faster and more accurate with help from AI. Code debugging — one of the most painstaking and unpleasant tasks for our developers — is now (usually) a breeze. Processing emails is dramatically faster with AI reviews and drafts. It is almost as if we have doubled our team size — for only $20 per month per team member in most cases. What AI makes possible with scaled data is nothing short of miraculous. A human can tell in a heartbeat whether or not one single-family home has a garage. But, to look at tens of thousands of them each day and do the same? There is no one human capable of or willing to deliver at that scale, but Large Language Models (LLMs) are happy to take on the task — and they can handle it in minutes and without complaint. LLMs have had a similarly profound impact on our analytical ability. We can feed financial statements into LLMs like ChatGPT, Claude or Gemini and query the financials to get insights in seconds. We can give the LLM an enormous data set and ask for any kind of analysis. They are not always right (like the humans they are built to emulate), but they make an invaluable thought partner in our work. While AI is becoming indispensable, it has serious limitations that are not going away anytime soon. Let’s break them down. Without Good Data, AI is Pointless (or Worse) AI is only as good as the data it uses — the challenge of “garbage in, garbage out” has not changed. Poor-quality data, incomplete datasets, or outdated information can lead to inaccurate predictions and flawed decisions. And SFR is rife with data challenges. Here are some common issues that AI runs into:  »            Offline Properties // In SFR, many properties exist solely offline — rented via a yard sign and managed in someone’s notebook, or an Excel sheet. As a result, AI will miss many reference properties that could be invaluable for analysis.  »            Data Fragmentation // Even when digitized, many owners and operators do so on in-house platforms that are not shared, so much of the data is behind enterprise firewalls and inaccessible to AI.  »            Old Data // SFR evolves rapidly due to factors like new developments, economic shifts, or regulatory changes. Too much data is historical and AI models may rely on old data without factoring in real-time updates.  »            Bias in Data // Data sets used to train AI models are often not statistically significant. These issues can be as simple as dramatically better data being available in one neighborhood or from one provider, causing that data to overwhelm other, potentially more valuable data, in the AI’s analysis.  »            Incomplete Data // I have never yet seen a property that is fully digitized. This is doubly the case for SFR properties, which are small in individual scale and highly varied. At best, the core characteristics are captured in the data, but there is always more missing than present in the digital record. Without extensive, representative, timely, and high-quality data inputs, AI is always going to struggle. So as users of these tools, we need to make sure that we can feed it the right data if we want to be able to depend on the outcomes. Missing Character, Intangibility, and Nuance I was first attracted to real estate by its very “real” character. Unlike most financial assets, real estate is a living, breathing thing with character and life all its own. This fact always hits home when I am touring properties, dating back to one of my first. I still remember that feeling of walking into a decrepit property in the northwest side of Chicago and seeing nothing but potential in the well-aged bones of an unusual property located in an edgy, but up-and-coming neighborhood. That very potential — wrapped up in very human concepts like character — is extremely difficult to digitize and, as a result, remains beyond the reach of AI in this space. Here are some examples of the most challenging gaps in understanding character for AI:  »            Neighborhood Sentiment and Future Growth // AI can analyze current demographic and economic data, but it may struggle to capture the subtle, on-the-ground shifts that can indicate future neighborhood growth. Factors like new businesses, planned infrastructure projects, or changes in community dynamics are much more visible to humans through local knowledge and experience than through data.  »            Property Condition and Renovation Quality // While AI can estimate the value of renovations or upgrades, it cannot fully evaluate the quality of craftsmanship, the durability of materials, or the aesthetic appeal of the property. Human judgment is crucial in evaluating whether improvements will attract residents or increase the property’s long-term value.  »            Local Market Nuances // Some SFR markets have hyper-local characteristics that may not be fully captured by data. For example, two neighborhoods within the same city could have vastly different demand characteristics due to local attractions, schools, or even intangible qualities like “curb appeal.” AI models tend to overlook these nuances, relying instead on broad averages. Over-Reliance on Historical Data AI models often depend heavily on historical data to make predictions about future performance. This reliance can be problematic in several ways:  »            Failure to Account for Disruptions // AI models may not be equipped to predict sudden changes in the real estate market, such as economic downturns, natural disasters, or major regulatory shifts. For example, during the COVID-19 pandemic, could AI models have predicted the spike in demand

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How Climate Risk Financial Modeling Will Change Real Estate Investing

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A Q&A with Toni Moss and Matthew Eby In April of 2024, REI INK was an official publication of the groundbreaking AmeriCatalyst conference, GOING TO EXTREMES: The Extreme Climate, Housing and Finance Leadership Summit. The event brought together the primary constituents in US housing to discuss risks posed by climate change to the housing market. Recent hurricanes Helene and Milton have emphasized the need for astute real estate investors to factor the impact of extreme weather on their investments. These multibillion dollar loss events have brought the vital conversation of insurance, climate migration and declining property values to the forefront. This article features a conversation between Toni Moss, founder and CEO of AmeriCatalyst LLC and Matthew Eby, Founder and CEO of First Street, about the emerging field of climate risk financial modeling (CRFM) and how it will fundamentally change the way real estate investors evaluate the impact of climate change on their investment portfolios. Toni: The historic precedent and ultimate impact of attributing climate risk scores to individual homes cannot be understated. A lot can be read about the science behind the scoring, but I’m interested in knowing about your original idea to do this. Matthew: During my time at The Weather Channel, I observed the difficulty of making climate change real in people’s everyday lives. The conversation often centered around melting ice caps and surface temperature rise rather than its direct impact on individuals and their families. This led to the question: How can we make climate change tangible? For most people, their home is their largest investment. By linking climate change impacts to this major financial and personal asset, we were able make the issue more real and urgent. Thus, eight years ago, we established First Street with the mission of connecting climate change to financial risk. We began by assembling the leading climate scientists in order to develop a set of comprehensive physical climate risk models focused on flood, wildfire, hurricane wind, air quality, and extreme heat. Next, through a collaboration with Arup, a leading environmental engineering firm, we were able to translate climate risk assessments to individual properties in a more tangible way, enabling us to quantify damage due to exposure. We now have property-level climate risk and damage assessments for every property in the US and are expanding internationally. First Street is the leading provider of climate risk financial modeling (CRFM) to governments and major financial institutions, and our climate risk data is featured on all the major real estate platforms: Realtor.com, Zillow, homes.com and Redfin. As a result, nearly every home on the market in the U.S. now includes our climate risk data. We consider this data crucial for everyone from homebuyers to real estate investors to large banks because it allows them all to make climate-informed real estate decisions. Toni: Now that First Street is on all three major real estate platforms (Redfin, Zillow, Realtor.com), what impact do you foresee these scores having on the housing market? Matthew: The integration of climate risk scores into the major real estate listing sites marks a pivotal shift in the housing market, elevating climate awareness to the forefront of real estate decision-making. By offering visible and accessible climate risk data to homebuyers and investors, it empowers them to make informed decisions on property purchases, significantly impacting buyer behavior. Prospective homeowners may now weigh climate-related threats against traditional factors such as price, location, and amenities, leading to a potential reshaping of geographic demand patterns. We have already seen areas identified as high-risk for floods, wildfires, or other climate-related events experience a decrease in property values, while regions deemed as “safer” from climate risk see an appreciation in value as demand increases. Our own research has found that by simply raising awareness around climate risk, there is an associated 3-4% property value decrease for at-risk properties versus those not at risk. Additionally, we have seen systematic changes in search history on sites where this data has been made available, with high risk property searches being followed by persistently lower risk searches. Moreover, as lenders incorporate climate risk scores into their risk assessments, this could lead to changes in mortgage lending practices. Properties in high-risk areas might face higher insurance premiums or stricter lending criteria, complicating the acquisition process. On a broader scale, the housing market will likely witness a recalibration with increased pressure for sustainable development and infrastructure improvements in vulnerable areas. This shift reflects a growing acknowledgment of climate change’s inevitability, urging all stakeholders—from government officials to developers to consider climate in their investing practices. Toni: Which poses a more significant risk from climate change: physical damage to structures or the secondary economic impacts such as insurance costs, property value, local GDP, and climate-related migration? Matthew: When evaluating the risks associated with climate change, real estate investors must consider both direct physical damage to structures and the secondary economic impacts that can arise more indirectly. While physical damage — such as property destruction due to flooding, hurricanes, wildfires, or other extreme weather events — poses an immediate and tangible threat, the secondary economic effects can be equally, if not more significant over the long term. Physical damage to properties can result in substantial repair costs and loss of rental income during downtime. Properties in high-risk areas face increased insurance premiums or even become uninsurable, affecting their overall profitability and marketability of both the property and the local community. Moreover, repeated exposure to climate-related hazards can lead to depreciation of property values, making it difficult to sell or rent them at competitive prices. Most recently, we have seen the first evidence of climate related migration in the US. Specifically, we are not seeing this as large macro migration trends of people fleeing risky areas, like Florida, Louisiana, and California, but instead are using property level climate risk information to identify properties within their desired residential locations. These decisions are being made by homebuyers armed with high-resolution and high-precision climate-risk information and, over time, are going to reshape

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How AI is Revolutionizing Private Lending

Embrace AI, Do Not Fear It by James Keegan Artificial Intelligence (AI) has crept into most industries in some way, and it is transforming not only the real estate industry overall, but private lending, specifically. As the private lending industry grows exponentially each year, lenders are facing a major rise in competition from new private lenders as well as banks that are actively expanding their lending offerings. From big data analytics to improved security, AI is a game-changer for private lenders who are striving to keep up with a rapidly evolving market and the changing needs of modern borrowers. AI technology is opening up a world where lenders can tap into these needs like never before and streamline the lending process, all while improving security and analyzing data, to create a seamless and unique experience for each borrower. Let’s take a closer look at some of the most impactful AI uses for private lenders as the future of real estate quickly becomes the present. Predictive analytics One of the most transformative ways that AI is being used in real estate in 2024 is to predict future market trends, property values and investment opportunities. Fast data analysis is perhaps one of AI’s most useful qualities, particularly for private lenders who can benefit from the deeper insights and hidden patterns that AI can deduce, which humans may not. AI is also more efficient at analyzing each neighborhood down to a granular level to identify changes that may impact properties and, by extension, real estate loans. A good example of this would be real estate appraisal companies that are now using AI for Automated Valuation Models (AVMs), to provide instant property valuations. Along with that, AI can predict future property values with much greater accuracy, which means that private lenders can assess the risk associated with properties more accurately and adjust their LTV ratios according to the future value of a property. Security and risk management AI is used to enhance security in real estate transactions by machine learning algorithms analyzing vast amounts of data in real-time to find patterns and pinpoint any anomalies. Through this, AI can detect fraudulent activities and, as such, ensure the integrity of transactions. AI systems can also detect fraudulent activities from small shifts in transactional data, which go unnoticed to the human eye, such as changes in digital signatures. This allows private lenders to keep up with fraudsters with ever-evolving tactics. Along with this, AI technology provides real-time risk scoring that adjusts according to the information gathered. So, risky accounts can now be identified quickly, which allows private lenders to intervene and manage these risks early to potentially save themselves from major losses. Streamlined application processing One of the most prominent enhancements that AI provides for the private lending industry is in the loan application process. AI is streamlining the way documents are collected and speeding up the entire loan application process which means that borrowers can get their loans quicker, with less paperwork and less stress. Loan processes are notoriously laborious and full of paperwork, but AI is optimizing this by reducing underwriting time through quicker borrower evaluations, extracting necessary information from documents, and verifying data using natural language processing (NLP) combined with machine vision. For example, AI technology can assess a borrower in just a matter of seconds by matching their income data with bank deposits and tax filings. Customer communication can also be customized to each borrower’s sentiment more accurately using AI, which can result in a stronger borrower-lender relationship. Automated documents and reports AI is being used to improve loan processes by automating the preparation of reports and documents. This not only speeds up the loan process but provides a transparent and efficient experience for borrowers. Loan agreements can be generated through AI using data from the loan application, the specific loan terms required, as well as the local laws and regulations that apply to each area. Smart contracts are one of the most revolutionary aspects of AI technology to be brought to the private lending realm. These are self-executing contracts that are triggered by certain actions and will execute according to pre-determined terms coded at the outset. Smart contracts are making loan transactions more efficient and hassle-free. Personalization In the age of individuality and customization, being able to offer loan products that can be tailored to each borrower’s specific needs is a pivotal moment for private lenders. Structuring loans in a way that suits each borrower in terms of their risk tolerance, financial situation and even their memory, is a great way to appeal to more clients and have more success with loan repayments. However, using AI, lenders can go one step further to create an overall lending journey that is uniquely designed for each borrower’s circumstances, goals and preferences. Beyond tailoring loan products to each borrower, AI allows for proactive customer service. For example, if a borrower is repeatedly missing their monthly repayments, AI technology could be used to pick up on this trend and proactively reach out to the borrower with a reminder each month. Alternatively, if the market conditions change within a borrower’s region, AI can be used to analyze the impact of this on the borrower’s loan and their loan structure can be adjusted accordingly. AI should not be seen as something to fear, but rather as a powerful tool for private lenders to use to stand out from the crowd by enhancing their existing processes, as well as introducing new features that are driven by data, personalization and proactivity. AI technology has created an eco-system where both lenders and borrowers can benefit from the transformative qualities it brings to real estate transactions and the lending journey. To learn more, please visit https://newsilver.com/

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Home Seller Profit Margins Drop Slightly in Q3

Typical Margin at 56% as Median Home Price Levels Out by ATTOM Team ATTOM, a leading curator of land, property data, and real estate analytics, released its third-quarter 2024 U.S. Home Sales Report, which shows that homeowners earned a 55.6% profit margin on typical single-family home and condo sales in the United States during the third quarter. That figure was down by small amounts both quarterly and annually, dipping by one percentage point from the second quarter of 2024 and two points from the third quarter of last year. The nationwide investment return ticked downward as home-price spikes that had buoyed the housing market during the Spring of this year flattened out, leaving the U.S. median home value virtually unchanged at about $360,000. While home-seller profits remain historically high, the national margin has declined almost every quarter from a 64% peak hit in 2022. The leveling off of prices during the third quarter also led to typical raw profits for sellers staying about the same, near an all-time high of just under $130,000. “The latest price and profit numbers provided another round of generally good news for homeowners, tempered by a bit of a downside,” said Rob Barber, CEO for ATTOM. “Home values remained at or near record levels around large swaths of the country, keeping seller profits far above historical levels. At the same time, though, the housing market settled down after a big second quarter, which extended a slow fallback in profit margins that started last year. If history is a good guide, the fourth quarter is likely to bring more of the same as the peak buying season ends.” He added that “this is far from a warning sign that the long market boom is ending. But there certainly are forces that could cut either way, especially as affordability remains a challenge for so many potential buyers.” Profit margins slip quarterly in half of U.S. Typical profit margins — the percent difference between median purchase and resale prices — stayed the same or decreased from the second quarter of 2024 to the third quarter of 2024 in 79 (50.6%) of the 156 metropolitan statistical areas around the U.S. with sufficient data to analyze. They were down annually in 112, or 71.8%, of those metros, and down in about the same portion since the second quarter of 2022, when the nationwide return on median-priced home sales peaked at 64.3%. Profit margins have softened over the past year throughout all price segments of the market, from metro areas where home values mostly sit below $250,000 to those where they top $450,000. But the low end of the market has fared a bit better. Typical margins decreased annually in about 60% of the least expensive metro areas compared to about 75% elsewhere. The biggest year-over-year decreases in typical profit margins during the third quarter of 2024 came in the metro areas of:  »            San Francisco, CA (margin down from 84.9% in the third quarter of 2023 to 61.4% in the third quarter of 2024)  »            Punta Gorda, FL (down from 94.1% to 74.4%)  »            Scranton, PA (down from 88.2% to 69.6%)  »            South Bend, IN (down from 77.3% to 59.2%)  »            Hilo, HI (down from 86.5% to 70.5%) Aside from San Francisco, the biggest annual profit-margin decreases in metro areas with a population of at least 1 million in the third quarter of 2024 were in:  »            Austin, TX (typical return down from 44.3% to 33.3%)  »            Honolulu, HI (down from 53.9% to 43.3%)  »            Riverside, CA (down from 78.6% to 69%)  »            Birmingham, AL (down from 52.1% to 42.7%) The biggest annual improvements in returns on investment came in:  »            Trenton, NJ (margin up from 65.5% in the third quarter of 2023 to 87.4% in the third quarter of 2024)  »            Albany, NY (up from 31.8% to 51.6%)  »            Rockford, IL (up from 54.5% to 70.2%)  »            Rochester, NY (up from 66.7% to 81.2%)  »            Evansville, IN (up from 47.2% to 61.7%) Two-thirds of metro markets show returns above 50% Despite the downward trend, returns on investment for median-priced home sales during the third quarter of 2024 still surpassed 50% in 107 of the metro areas analyzed (68.6%). That was down from three quarters of those areas in the third quarter of last year but far above the level of 13% five years ago. The leaders among areas with a population of at least 1 million in the third quarter of this year were:  »            San Jose, CA (typical return of 109.8%)  »            Seattle, WA (90.3%)  »            Providence, RI (84.6%)  »            Miami, FL (83.9%)  »            Grand Rapids, MI (81.9%) The lowest among areas with a population of at least 1 million were in:  »            New Orleans, LA (24.8%)  »            San Antonio, TX (25.1%)  »            Austin, TX (33.3%)  »            Houston, TX (37.3%)  »            Dallas, TX (37.4%) Raw profits remain near record level The raw profit on median-priced home sales nationwide, measured in dollars, slipped 0.9% during the months running from July through September of this year, to $128,700. But it was still up 2.7% from the third quarter of 2023 and remained near the record of $135,000 hit in 2022. Typical raw profits were flat or down quarterly in 74, or 47.4%, of the markets analyzed. Despite the nationwide year-over-year gain, raw profits were the same or down annually in 82, or 52.6% of those metro areas. The biggest year-over-year increases in raw profits on typical sales among metro areas with a population of at least 1 million were in:  »            Rochester, NY (up 24.4%)  »            Cleveland, OH (up 23.5%)  »            Providence, RI (up 18.9%)  »            Chicago, IL (up 18.8%)  »            Cincinnati, OH (up 15%) National median home value stalls in Summer of 2024 Nationwide, the median price of single-family homes and condos rose from the second to the third quarter of 2024 by just 0.2% after spiking 7.4% in the Spring. But it still hit a new record of $360,500, up from $359,900 in the prior three-month period.

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Home Flipping Activity Dips Slightly

Profits Inch Up Across U.S. in Second Quarter of 2024 by ATTOM Team ATTOM, a leading curator of land, property data, and real estate analytics, released its second-quarter 2024 U.S. Home Flipping Report showing that 79,540 single-family homes and condominiums in the United States were flipped in the second quarter. Those transactions represented 7.5%, or one of every 13 home sales, nationwide during the months running from April through June of 2024. The latest portion of flipped properties was down from 8.7% of all sales in the U.S. during the first quarter of 2024 — a common pattern during the busy annual Springtime buying season each year when other types of home sales spike. The flipping rate was also down slightly from 7.9% a year earlier. While the rate declined, fortunes kept ticking upward for investors who buy, renovate and quickly resell homes. The latest data showed that investors typically earned a 30.4% profit nationwide before expenses on homes sold during the second quarter of this year, marking the fourth time in five quarters that margins increased following a six-year period of nearly continuous drop-offs. The typical profit margin on homes flipped during the second quarter of 2024 — based on the difference between the median purchase and median resale price for home flips— remained about 25 percentage points below peaks hit in 2016. It also stayed within a range that could easily be wiped out by carrying costs that include renovation expenses, mortgage payments and property taxes, revealing anew the struggles home flippers are having in turning healthy profits. But the return on investment was up slightly from both the first quarter of 2024 and from a low point over the past decade of about 25% in the first quarter of last year. Gross profits on typical flips around the country, meanwhile, increased to about $73,500. That remained down from a high of almost $81,000 reached in 2022, but up from $70,000 in the first quarter of 2024 and more than $12,000 above last year’s low point. “The Spring home-buying season of 2024 brought another sign of hope for home flippers that the rebound in fortunes that began for them last year was more than just a temporary thing,” said Rob Barber, CEO for ATTOM. “It’s not as if profits have shot through the roof and investors are riding a new wave of good times. Far from it, as they continue to struggle to benefit from the broader market boom. But the second-quarter numbers did show another step in the right direction.” He added that “with the market rising amid tight supplies of homes for sale around the country and falling interest rates, conditions appear ripe for more improvement over the rest of the year as long as prices don’t shoot up past what most buyers can afford.” The small changes in flipping activity and profit margins during the second quarter came during yet another period of mixed patterns for the home-flipping industry compared to the U.S. housing market. Overall, home prices rebounded strongly during the second quarter from a varied period of gains and losses during the prior 12-month period. Median prices for all single-family homes and condos nationwide rose 9% quarterly and 6% annually. But home-flipping resale prices rose far less, with the median inching up only 2% quarterly and annually to $315,000. Nevertheless, that was enough to boost flipping profit margins as investors benefitted, in small increments, from shifts in prices going in their favor between the time of purchase to resale. Those gaps led to the quarterly and yearly improvement in investment returns. The latest gains for home flippers extended their recovery from an unusual pattern of timing the housing market poorly, which resulted in their profits dropping from 2016 through 2022 while returns for other sellers soared. Home-Flipping Rates Dip Downward Home flips as a portion of all home sales decreased from the first quarter of 2024 to the second quarter of 2024 in 159 of the 185 metropolitan statistical areas around the U.S. with enough data to analyze (85.9%). They went down annually in 115, or 62.2%, of those markets. Measured against the same peak buying period of 2023, most flipping rates declined less than one percentage point. (Metro areas were included if they had a population of 200,000 or more and at least 50 home flips in the second quarter of 2024). Among the metro areas analyzed, the largest flipping rates during the second quarter of 2024 were in:  »         Warner Robins, GA (flips comprised 20.7% of all home sales)  »         Macon, GA (15.4%)  »         Atlanta, GA (13.4%)  »         Columbus, GA (13.2%)  »         Memphis, TN (12.8%) Aside from Atlanta and Memphis, the highest second-quarter flipping rates among metro areas with a population of more than 1 million were in:  »         Birmingham, AL (11.7%)  »         Cleveland, OH (11%)  »         Columbus, OH (10.7%) The smallest home-flipping rates were in:  »         Hilo, HI (3.3%)  »         Honolulu, HI (3.5%)  »         Seattle, WA (4%)  »         San Jose, CA (4.1%)  »         Portland, OR (4.2%) Typical Home-Flipping Returns up Y-O-Y The median $315,000 resale price of homes flipped nationwide in the second quarter of 2024 generated a gross profit of $73,492 above the median investor purchase price of $241,508. That resulted in a typical 30.4% gross profit margin before expenses in the second quarter of 2024, up about one point from 29.2% in the first quarter of 2024 and up from 27.8% in the second quarter of last year. But the latest nationwide figure still remained far beneath the 56.3% level in mid-2016 and from a more recent peak of 48.8% in 2020. Profit margins increased from the first to the second quarter of this year in 93 of the 185 metro areas analyzed (50.3%) and were up annually in 107 of those markets (57.8%). Metro areas with the biggest year-over-year increases in typical profit margins during the second quarter were:  »         Akron, OH (ROI up from 30.9% in the second quarter of 2023 to 78.1% in the

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