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Real Estate Big Data

What the NAR report says about characteristics of home buyers and sellers

(REAL ESTATE BIG DATA) The National Association of Realtors annual report is out, and now we have the breakdown of the characteristics of this year’s home buyers.

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The National Association of Realtors® (NAR) released its most recent Profile of Home Buyers and Sellers report. The annual survey conducted by NAR “allows industry professionals to gain insight into detailed buying and selling behavior.” This year’s survey contained 131 questions, and it surveyed home buyers and sellers who purchased between July 2019 to June 2020.

Here is a breakdown of some characteristics of home buyers.

Median age of home buyers

A shift in a home buyer’s age hasn’t changed much when compared to last year. For first-time home buyers, the median age is still at 33 years. Repeat buyers have remained at 55 for three straight years. The median age of home buyers stayed at 47 in 2019 and 2020, which has been the oldest median range since NAR began collecting data in 1981.

Also, the 25 to 34 age group continues to be the largest share of home buyers. This year they accounted for 23% of all buyers, and the smallest share of home buyers came from the 18 to 24 (3%) and 75 and over age groups (5%).

Median Age of Homebuyers 1981-2020

Decrease of first-time home buyers

First-time buyers’ market shares have remained below the historical norm of 40% since 2011, and this year we saw a new decline. In 2019, first-time buyers made up 33% of all home buyers. This year that figure dropped to 31%. This is the lowest it has been since 1987 when it was at 30%.

First Time Home-Buyers

Increase in median household income

First-time buyers might have decreased, but median household income for 2019 increased. This year the median income is at $96,500 whereas last year’s median income was at $93,200.

Although income has increased for both first-time buyers and repeat buyers, there is a large gap between the two. Data shows first-time buyers’ median income is at $80,000, and repeat buyers’ median income is at $106,700. Also, married repeat buyers have the highest median income at $120,300.

Household Income of Home Buyers by Region 2019

The report shows those with higher incomes come from the West region with the Northeast region following right behind. And, this could be the reason the Northeast has the highest share of first-time home buyers with 37%. And, why the South region has the lowest at 28%.

First Time Home Buyers by Region

Who’s buying a house?

Married couples make up 62% of recent buyers, up 1% from the year before. Single female buyers make up 18%, and 9% are single male buyers. However, the shares of first-time buyers that were married decreased from 53% to 52%. For married repeat buyers, it remained at 67%. And, first-time buyers who are unmarried couples decreased to 16%.

Adult Composition of Home Buyers 1981-2020

Why are people buying houses?

Not surprisingly, the main reason first-time home buyers want to purchase a home is that they want to have a house they can call their own. According to the report, 64% of first-time home buyers said this is why they purchased a house.

  • On the other hand, repeat buyers purchased for these reasons:
  • To purchase a larger home – 13%
  • To move closer to family and friends – 13%
  • Due to a life change (childbirth, marriage, or divorce) – 9%

Primary Reason to Purchase a Home, First Time and Repeat Buyers

Home buyers are also purchasing homes because it happens to be just the right time. Of all buyers, 51% cited this as a reason. For first-time buyers, they were at 63% and repeat buyers at 45%. Also, 15% of buyers said they didn’t have another choice, and 12% bought a house because of the availability of homes for sale.

Primary Reason for Timing of Home Purchase, First Time and Repeat Buyers

Also, 12% of all buyers are still purchasing multi-generational homes like they did last year. These homes have “adult siblings, adult children over the age of 18, parents, and/or grandparents” living in the household.

  • The main reasons for purchasing this type of home are:
  • Take care of aging parents – 25%
  • Children over 18 moving back home – 19%
  • To save money – 16%
  • Spend more time with aging parents – 16%

Home Purchase was Multi-Generational Home (Will Include Adult Siblings, Adult Children, Parents, and/or Grandparents)

NAR’s report shows the 25-34 age group still maintains itself as the largest age group of home buyers. The number of first-time buyers has decreased, but this can be expected as the pandemic has turned things upside for a lot of people. However, there has been an increase in overall median income, and the data shows people still have a desire to own their own home, including buying multi-generational homes.

Veronica Garcia has a Bachelor of Journalism and Bachelor of Science in Radio/TV/Film from The University of Texas at Austin. When she’s not writing, she’s in the kitchen trying to attempt every Nailed It! dessert, or on the hunt trying to find the latest Funko Pop! to add to her collection.

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Real Estate Big Data

Shadowmap shows where the sun and shadows are at any property, any time

(TECHNOLOGY) It seems like such a small detail, but for a client desperate to know where the sun and shadows are for energy efficiency, gardening, or what have you, Shadowmap is your tool for an immediate answer.

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For something that we all experience daily, sunlight can feel oddly personal – especially if you don’t feel like you’re receiving enough of it at the right time in your house. If you’ve been in the market for new data or a tool to help draw in real estate clients – or just appeal to your current ones – Shadowmap may be your saving grace.

Shadowmap, a tool from creators George Molzer and Simon Mulser, is a deceptively simple product with some really cool applications: It allows you to map out solar shadows in any environment using “3D data in a worldwide interactive maps app.”

The base program itself is free for online use, and it’s extremely easy to use. Simply typing in an address will take you to the property in question, at which point you can drag a slider to simulate the passage of time throughout the day.

Shadowmap’s “Pro” subscription comes at around $10 per month (or $100 per year) and allows you to select specific dates (both past and future) as well as view high-definition visuals; you’ll also receive updates as they come.

Adding this to your toolbox could help convince clients who are on the fence about purchasing certain properties, and it makes for a powerful discussion piece when taking on new clients.

Being able to tell someone exactly how the sun will illuminate certain sides of their property after only a few seconds of research is impressive, and it entails information that many consumers will want to hear whether or not it is at the forefront of their minds.

There are plenty of applications for developers and casual designers, too. Product Hunt cites several different users proclaiming their joy at the creation of Shadowbox, from people who are excited at the idea of Feng Sui to relieved developers positing that the tool will make compliance easier to achieve.

Employees who work in the elements are similarly intrigued: “As I work with Wine Imports the use-case for weather-sensitive products such as Rosé are clear,” says Andreas Bøggild.

Of course, the technology is only as accurate as past weather conditions can dictate, but Shadowmap is a welcome alternative to standing in the garden and looking up at the sky for hours to see when the sun will finally hit the living room window.

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Real Estate Big Data

After four months of decreasing home sales, there’s a 1.4% blip upwards

(REAL ESTATE) With massive annual gains and slight monthly gains, existing home sales rise 1.4% in June, but the market remains plagued by tight inventory levels.

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Between May and June, existing home sales (contracts signed) rose 1.4%, after fourth consecutive months of declines and a 22.9% increase from June 2020, per the National Association of Realtors (NAR). The average days on market nationally was only 17 days.

After months (years) of restricted inventory levels, and an entire sector holding their breath for any relief, inventory levels rose 3.3% in June. The market remains blue in the face while holding said breath, but even this slight improvement is a sign of hope.

Meanwhile, the median home price hit $363,300, rising 23.4% over the year, the second highest increase on record since January 1999. All-cash transactions are increasingly common as folks increase their wealth by cashing out of the stock market and/or their housing equity, continuing to nudge out many first-time buyers who rely on traditional mortgage options (despite interest rates remaining historically low).

NAR’s Chief Economist, Dr. Lawrence Yun said, “Supply has modestly improved in recent months due to more housing starts and existing homeowners listing their homes, all of which has resulted in an uptick in sales. Home sales continue to run at a pace above the rate seen before the pandemic.”

Dr. Yun noted that home prices won’t decline with still-tight inventory conditions, but expects price appreciation to slow by year’s end. “Ideally, the costs for a home would rise roughly in line with income growth, which is likely to happen in 2022 as more listings and new construction become available.”

Existing home sales in the Northeast rose 2.8% in June, 3.1% in the Midwest, 1.7% in the West, and remain unchanged in the South.

So what’s next for the housing sector with rising prices and restrictive inventory levels?

“NAR continues our conversations with policymakers and leaders from across the industry in an effort to boost housing inventory and increase access to safe, affordable housing for all Americans,” said NAR President Charlie Oppler. “As the nation’s economy continues to recover from COVID-19, securing policies that are in the best interest of U.S. consumers and homeowners remains NAR’s priority.”

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Real Estate Big Data

5 ways AI is shifting real estate and how to capitalize on it

(REAL ESTATE BIG DATA) Artificial intelligence is bringing a seismic shift to commercial real estate in everything from investing to sales to property management. Hold on!

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Woman working at desk with multiple desktops open to AI tools.

Forget about that location thing. Now real estate – especially commercial real estate – is about data, data, data. As in, Really. Big. Data. And AI is owed a large part of the credit for that.

A dizzying amount of data is being crunched and sorted and searched by artificial intelligence-enabled tools that are changing how deals get done and who will still have a job in the future.

The promise of AI to use data to predict the future is massive – and it promises to do that with more accuracy and efficiency, greater productivity, and less cost for commercial as well as residential real estate.

So, what, exactly, can AI do for commercial real estate? Let’s break it down.

What AI is

To put it simply, artificial intelligence is what lets Amazon’s Alexa talk to you and cars drive themselves. Its algorithms use data to mimic human intelligence, including learning and reasoning. Then there’s machine learning, where algorithms analyze enormous amounts of data to make predictions and assist with decision making. We’re putting them both under the same AI umbrella.

There are four main areas where AI is remaking the commercial real estate industry: development and investing; sales and leasing; marketing; and property management.

Development and investing

With its ability to quickly analyze a staggering amount of data, AI lets investors and developers make better data-driven decisions. More responsive financial modeling helps identify ideal use cases and project ROI under multiple scenarios using real-time data. Pulling in alternative data – say, environmental changes or infrastructure improvements – goes beyond traditional data points and can identify investment opportunities, such as neighborhoods beginning to gentrify. In fact, alternative, hyper-local data has become even more important as COVID-19 continues to upend property valuation models.

AI’s crystal ball comes from recognizing patterns in the data and continuing to learn from new information. It can forecast risk, market fluctuations, property values, demographic trends, occupancy rates and other considerations that can make or break a deal.

And it does all of this more efficiently, more accurately and less expensively than manual methods.

Sales and leasing

There’s a big question looming over AI and automation: Will technology put real estate brokers out of business? The short answer is, “No, but brokers need to step up their tech game.”

Keeping up with – and being open to – tech trends is essential. Clients’ ability to use online marketplaces to search for or list property will only grow, but there still is no substitute for expertise and the personal rapport that builds trust. Chatbots can’t negotiate (yet). Robots can’t show a space and weave details about the property into a story. (If you want to know more about using storytelling in real estate, check out this great marketing guide.)

But Big Data is such a powerful tool that brokers need to know how to harness it for themselves. Having more, and more nuanced, data about clients and properties means brokers can better match the two. They can be more confident in setting sales prices and rental rates. Becoming a “technology strategist” to help clients design an automation strategy for a property would be a great value add to their services. Even just starting out with a website chatbot to answer common questions would add a level of tech-savvy efficiency to communication with clients and prospects.

Marketing

Also a boon of Big Data for brokers: more sophisticated, targeted marketing for themselves, as well as for client properties.

Integrating AI with customer relationship management (CRM) tools brings a richer understanding of clients and prospects that can make choosing marketing channels and personalizing targeted content more precise.

Then there’s data-driven lead scoring. Property intelligence firm Reonomy says its commercial data mine – 52 million properties, 100 million companies, 30 million personal profiles, and 53 million tenants – can be searched in multiple ways to create custom prospect lists. (Check out Forbes.com’s “5 Ways Artificial Intelligence is Transforming CRMs” for a fascinating list of what AI can do, including analyzing conversations for sentiment analysis.)

Property and facility management

The Internet of Things (IoT) is already helping property and facilities managers control and predict energy costs, as well as proactively address maintenance issues. Integrating smart technology like thermostats and sensors with AI also means more efficient space planning. Smart security cameras and wi-fi tracking can create “people heat maps” that can identify underutilized or overcrowded areas.

IBM’s TRIRIGA does that and more. Part of the Watson project, TRIRIGA offers AI-driven insights to show how people are actually using a space and ensure a company has the right amount of space in the right areas. It can also analyze common questions from a chat log, then use that data to create an AI virtual assistant to automatically answer those questions – and update itself as it learns new data. Maintenance requests, room reservations and more can be fully automated.

Strategic space planning has become even more important during the pandemic, as work-from-home trends and safety concerns reshape offices as workers return. (Need ideas for your office? IBM’s Returning to the Workplace guide might be a good place to start.)

Barriers to adoption

There’s no question tech-enabled commercial real estate companies will have a competitive edge. The question is, when will more of them agree enough to adopt AI more widely?

PropTech with and without AI has exploded over the past few years – and that’s part of the problem. In an Altus Group survey, 89% of CRE executives said the PropTech space needs significant consolidation before it can effectively deliver on industry needs; 43% said that is already underway or will occur within 12 months.

Then there’s the undeniable learning curve that comes with any tech tool – an investment of time as well as money. The survey also showed concerns about regulatory requirements for data collection and management, having enough internal capacity, and nonstandard data formats.

Despite those perceived barriers, there’s also no question that innovation and disruption from AI are moving at a dizzying pace – and that commercial real estate needs to keep pace.

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