From Blueprints to Reality:

Social listening reveals how millennials are building, rather than buying, new homes

Henry Chapman, Research and Insights Analyst

New homes and a new API

Buying a home has been a significant milestone for many young adults in their twenties and thirties in the United States. But nowadays, homebuying has become incredibly difficult. Home loan interest rates are increasing, and there are fewer houses available due to limited construction and zoning limitations, a hangover from the Great Recession of 2008. Mainstream news outlets have covered how millennials and Gen Z have gotten very anxious about their home-buying prospects. However, after looking at the data, we've found that while a considerable percentage of home-buying conversation comes from younger Americans, the talk is less negative than the mainstream news would lead you to believe. In fact, millennial Americans are surging into new home builds. This insight highlights a well-known use case for social listening data: using it to prove or disprove familiar narratives in the news.

We're incredibly excited about this piece because we're introducing data visualizations created with our upcoming Infegy Starscape API. This newest iteration enables custom nested aggregations, something that was previously unavailable. In data science or databases, aggregation means taking a bunch of information and grouping it together to get useful summaries. Within our social dataset, we have tons of fields (source age, source gender, publish date, etc.). With our new API, we can gather all the posts that are of the same type like people aged 20-40. We can then generate stats (count, mean, median, percentile, etc.) based on these different groups. Custom aggregations mean we can tell better and more flexible stories with even larger and more diverse datasets.

Surging post volume by age

Let's start by looking at how many posts people of different ages have made about home buying. We can do this thanks to Infegy Starscape's all-new API, which enables nested aggregations across any of our database's fields. In the graph below, we've divided the posts made over 16 years on all of Infegy's social channels into four age groups: 20 to 40, 40 to 60, 60 to 80, and 80 to 100.

Aggregated age breakdown by month using Infegy's new Starscape API
Figure 1: Aggregated age breakdown by month using Infegy's new Starscape API (2007 through 2023); Infegy Starscape data.

Here are three takeaways from this graph:

  1. The age group of 20 to 40 posted the most about home buying. This finding isn't surprising because most people considering buying a home fall into this age range, typically in their late twenties to early thirties.
  2. Even though the volume of posts varies between age groups, different age groups home-buying conversations are highly correlated. This correlation means that while younger folks discuss home-buying more often than older folks, all age groups tend to talk about it around the same time over the past 16 years.
  3. The volume of posts about home buying changes significantly during major national events. One example is the fallout from the Great Recession of 2008, which caused a sharp drop in discussions about home buying in January 2009. More recently, in March 2020, we saw a massive increase in home-buying discussions, especially among millennials. This surge happened because, in March 2020, most parts of the United States went into lockdown due to the pandemic, and many millennials found themselves stuck in small apartments. The pandemic led to a higher demand for homes, driving up prices, which still affects the housing market today.

Positivity associated with younger social media users

Now that we've examined the overall number of posts, let's delve into how Emotions vary across different age groups. We can do this thanks to Infegy Starscape's custom groupings. In Figure 2, we analyze how frequently posts from each age bracket express specific emotions. In this analysis, we're not just counting the number of posts but looking at how common a particular Emotion is within each age group.

For the youngest age group, we noticed that they primarily expressed positive emotions like Joy, Anticipation, Trust, and Love. Anticipation, in particular, was the most common emotion, appearing in 30% of their posts. However, as we move to older age brackets, we see Anger becoming more prevalent. It's present in 20% of posts for individuals aged 40 to 60 and nearly 50% of posts from those aged 60 to 80. These percentages challenge the common belief that millennials are overwhelmingly pessimistic about home-buying.

Image 2 - Emotions by Age
Figure 2: Aggregated Emotion distribution by age range using Infegy's new Starscape API (2007 through 2023); Infegy Starscape data.

Narratives reveal interest in new home builds

We wanted to understand the positivity associated with millennial home buying. To do so, we turned to Narratives to get a better feel for the conversational context. Narratives links topics across disparate documents to build clusters of conversation. We found many of the clusters you'd expect around home buying - the most significant cluster dealt with investment performance. In contrast, another cluster talked about buying a home in the landscape of rising interest rates.

However, we also found a cluster about potentially building a home versus buying one. Interestingly, this cluster had a median age of 34 and a positivity rating of around 80%. This median age is precisely within our target research demographic and helps explain why millennial home buyers are more favorable than expected. Millennials building instead of buying older homes is becoming a common trend within real estate.

Narratives associated with home buying
Figure 3: Narratives associated with home buying (2018 through 2023); Infegy Atlas data.

New home builds post volume

We wanted to look into this trend. We searched for conversations of new home builds and found a 60% surge in post volume since September 2020. This finding matches what the press has widely reported in states like Iowa, Texas, and Arizona. Interestingly - social listening data corroborates the interest in those states as well. We found surging post volume in each of those around the topic.

In the world of rising interest rates, people locked in mortgages at 2-3% are unwilling or unable to move and give up those rates. As a result, there are much fewer used homes on the market. Millennials are turning to new home construction to meet their housing needs.

Post volume associated with new home builds
Figure 4: Post volume associated with new home builds (2020 through 2023); Infegy Atlas data.
Post volume by state associated with new home builds
Figure 5: Post volume by state associated with new home builds (2020 through 2023); Infegy Atlas data.

High millennial positivity

To corroborate our hunch that the driving force behind this new home build positivity is coming from millennials, we looked at the age distribution attached to this query. We found that 25 - 44-year-olds comprised 47% of the post volume attributed to new home building. We also found that 35-44-year-olds lead the positivity score with 81%.

Age distributions associated with new home builds
Figure 6: Age distributions associated with new home builds (2020 through 2023); Infegy Atlas data.

Takeaways for your social listening practice

Our analysis of millennial home buying trends through social listening data uncovered a surprising and more positive perspective than commonly portrayed. This data challenges prevailing narratives, showing that millennials and younger generations actively engage in home-buying discussions that often express positivity. We've also identified an emerging trend: millennials' growing interest in new home construction. Social listening provides a powerful lens through which we can gain deeper insights into customer behavior, enabling us to make informed decisions and stay ahead of the curve in a rapidly changing market landscape, knowledge that is especially for real estate companies, or any businesses looking to cater to their evolving customer preferences.

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