Through our down payment program, Landed co-invests with essential professionals, starting with educators (teachers and other school staff), to buy single-family homes (including stand-alone buildings, townhomes and other dwellings like condos) in the largest metropolitan areas of the United States, including the metropolitan areas of Los Angeles, San Francisco, San Diego, Portland (Ore.), Seattle, Honolulu, Denver, Boston, and Washington DC. While these partial investments in single-family homes make for a unique and compelling way for pension funds and other institutional investors to allocate the part of the wealth they manage dedicated to 'real assets' (like real estate), they also deliver meaningful impact to (a) the educators we serve, (b) the students they teach, and, (c) the community at large.
As we approach supporting our 500th family, the following report breaks down Landed’s impact in relation to these three distinct groups: educators, students, and communities. Enormous shout-out to Landed's Investment Analyst Louis Odette for much of the hard work that's gone into producing this report, as well as to the entire force of Landed's proverbial 'village' who have made all of what's in this report possible: Landed teammates past and present, supporters, and key partners.
- If programs like Landed were ubiquitous, preliminary data implies that (a) the homeownership racial gap would be significantly reduced, (b) 40% more middle-income families would be able to buy homes without public assistance, saving precious housing assistance for low-income families, and, (c) employees would live closer to work, saving nearly half-an-hour in daily commute times.
- Educators who buy homes with Landed are expected to leave education at a meaningfully lower rate than their peers: (1) The reduction in turnover saves up to $22,000 per home purchase in turnover costs for an employer, and, (2) the reduction in turnover is shown to have a significant effect on improving student achievement in both math and English language arts (ELA).
Landed’s Theory of Change
At Landed, we believe in upholding those who uphold us, and thus essential professionals should be able to build financial security near the communities they serve. We believe achieving our mission requires essential professionals have compelling, achievable options for local homeownership.
We believe that homeownership is an attractive option for many individuals and that a community with many homeowners is more resilient than a community dominated by renters. This is particularly important in the United States where tenant protections often lag other developed countries.
Despite their desire to be homebuyers, many households that have enough monthly income to pay for a monthly mortgage still struggle to buy a home because the required, one-time down payment is too large: limited cash savings make the down payment hurdle insurmountable.
We believe that this need for low down payment financing is best fulfilled by an alternative to high loan-to-value (LTV) mortgages, like shared equity. Homebuyers would greatly benefit from equity (or quasi-equity) investment from experienced third-party providers (like Landed), who facilitate sharing in the financial risk and reward of homeownership. Otherwise, homeownership may be unattainable for more and more people. Where it is attainable through government-sponsored programs or private mortgage insurance, mortgages requiring less down payment (like 3% or even 0% down options) increase the monthly cost and risk of buying a home for the individual homebuyer.
Impact Group 1: Educators
Landed has led calls with over 3,200 educators to coach them on their home buying options. For those whom Landed’s down payment program (DPP) did not make the most sense, or better local programs were available from governmental or non-governmental entities, Landed helped educate these individual educators on their other options.
Landed's nearly 500 educators who used our DPP have bought over $300M worth of homes across the country. You can explore a sampling of these purchases using the map below. Note: the homes have been mapped only to their zip code to protect the privacy of individual educators.
Over the length of time that these educators have owned these homes, they have also been building wealth. Landed has helped the educators who purchased a home through our DPP build almost $15M of wealth through homeownership.
Given that the average Landed educator has only been a homeowner for a year, this represents approximately $30,000 of wealth gained per year per participant.
Assuming that these gains are likely to stay tax free, and assuming that our educators were likely to be holding the money they were saving for a down payment in a savings account, earning $30,000 of wealth per year could be considered the equivalent of receiving a $40,000 to $50,000 salary increase. While educators unequivocally deserve increased compensation, there are additional ways to increase financial security and wealth on top of solely making changes to income.
“Denver has become very expensive to live in and having comfortable monthly mortgage payment seemed impossible for me. I wanted to buy a home but adding HOA [homeowner association] fees and MIP [mortgage insurance premium] on a monthly mortgage payment was way over my comfortable monthly payment. I decided to use Landed for the purchase of my home, because I didn’t have the 20% down payment available to avoid the MIP, and also lower my monthly payment without a loan that I have to make monthly payments on. I was able to close my home with no MIP and no HOAs in my dream neighborhood, and mortgage payment way lower than the monthly rent in Denver. Would highly recommend Landed to other teachers.” - Educator & Landed customer
Impact Group 2: Students
Within the urban geographies where Landed operates, public school students are almost 75% non-white. In these geographies, over 50% of public school students qualify for Free or Reduced Price Lunch (FRPL). Improving the quality of education these students receive is foundational to improving both racial equity and income mobility in the United States.
Supporting educators to buy homes improves public education primarily by reducing staff turnover. Here's the logic:
- Higher staff turnover tends to result in less experienced instructors. Less experienced instructors, especially those in their first few years of teaching, deliver worse instruction.
- Even if the experience of leaving instructors is equivalent to the replacing instructors, turnover is difficult for schools to manage resulting in a significant and negative impact on student achievement in both math and English language arts (ELA).
- Even if the experience of leaving instructors is equivalent to the replacing instructors, and the turnover is managed perfectly, the cost of turnover has been shown to be as much as $22,000 per teacher which could be spent on many other things to improve the education quality.
District-by-district staff turnover rates are challenging to gather. In Colorado, where this data is public, the districts in which Landed operates average a 20% turnover rate. Recent reporting also suggests that San Francisco Unified School District has a turnover rate of 21%. In all, we estimate that the districts and counties we serve have staff turnover rates between 13% and 25%, meaning that the average staff member stays only 5 years.
According to Landed’s post-home-purchase surveys, educators supported by our DPP plan to remain educators for an additional 13 years, resulting in an expected annual turnover rate of 7.7%. When asked how long they would have stayed if they hadn’t been able to buy a home, respondents answered 3.5 years.
"I intended on staying with my employer as long as they'd have me, but life happens. If it got too tough, I would move since there was nothing really tying me to this place beyond the job. But now, and really for the first time in my life, I have roots in a community I can now, and hopefully forever more, call home. It would not have been possible without Landed, so MAHALO!!!” - Educator & Landed customer
Impact Group 3: Communities
Why is homeownership so central to the discussions on wealth inequality between people of different races? As an example: if better policy and programs could eliminate the differences in both (a) homeownership rates between races, and (b) the financial gains from homeownership between races, it would reduce the racial wealth gap by between 45% and 70%.
Early on in our work at Landed, we noticed that in the San Francisco Bay Area (where Landed got started), about 43% of the households we were supporting into homeownership were Black, Latinx, or Asian Pacific Islander. That compared to 12-13% for this same group when you looked at who, on average, was able to buy a home in the most expensive real estate market in the country. The implication of this highlights the role homeownership has in discussions about racial wealth disparities in the United States.
One cause for sustained differences in the homeownership rate between races is that homeownership is an inherited property: the family home (or the financial value of it) is often passed down from generation to generation. Systemic, federally endorsed racism prevented most black and brown families from using homeownership as a tool to wealth build for generations, providing one of the reasons why there is such an imbalance of access to inherited property along racial lines. The children of homeowners are 7% - 8% more likely to become homeowners than children of renters. In addition, the wealthier your parents, the more likely you are to be a homeowner, all else being equal.
One hypothesis that we’ve had since starting Landed is that by reducing down payment requirements, those using a down payment program like ours would be less dependent on accessing parental wealth to become homeowners: you wouldn't necessarily be required to have access to a 'bank of mom and dad' to have enough savings for a down payment. This, in turn, would mean those who purchased their home with the support of Landed's DPP would more closely resemble the racial demographic makeup of the total population of the geographies we serve when compared to the traditional mortgage originations market. As the figures below demonstrate, this is exactly the case:
Supporting the Middle Class
Because of the important role that homeownership plays in the lifetime financial journey of Americans, we believe that it will always be a target of public policy. Recently, there has been a lot of debate as to whether public housing programs that have traditionally been limited to families making less than 80% of area median income (AMI) should be extended to families making up to 120% of AMI. Such an expansion is meant to better support middle-income families who increasingly are having a difficult time accessing homeownership in the free-market, but this would come at an enormous public cost: the housing dollars that would otherwise go to support the poorest and most vulnerable among us would instead be transferred to these programs, and/or, the sheer volume of dollars needed to meaningfully make an impact on this issue is bigger than the public is willing to transfer over.
At Landed, we’ve found that private down payment programs like ours do increase access to homeownership for middle-income families without requiring government funding. When compared to the standard market of people accessing a mortgage to buy a home, educators who used Landed's DPP are 40% more likely to be middle-income families.
“Landed was the right opportunity for us. It seemed we made too much money in most other options we would look into, especially for down payment assistance. However, in our lived experience it was a great challenge to save up a full down payment where the cost of living is so high. By using Landed, we were able to avoid PMI [private mortgage insurance], have a more competitive offer with a 20% down payment, and have lower monthly mortgage payments. This all made it possible for us to buy a single-family house in the neighborhood we really wanted, rather than having to settle for a condo or a neighborhood that was further from our target neighborhood. I think it's a really good option for people in a similar situation to ours.” - Educator & Landed customer
Decreasing Environmental Emissions
Recently, there have been many conversations about the peril of educators becoming super commuters in order to manage housing costs. And while we have not been able to find a structured dataset of educator commute times for every county in which Landed operates, we have been able to compare the commute times of the educators who used our DPP to all other commuters in the same counties.
We’ve found that on average educators who used Landed's DPP enjoy 12 minute shorter commutes, each way, and that the amount of super commutes amongst these educators is less than a quarter that of the average commuter (6% for educators using Landed DPP vs 26% for the average commuter).
“I chose to work with Landed because it allowed me to purchase a home much closer to my work, in an area I would not have been able to live had I not used Landed.” - Educator & Landed customer
We’ve gained a much better understanding of the role of shared equity down payment programs by building Landed's version to date. While such programs can often be difficult to administer (our operational learning has been enormous), the data is painting a clearer and clearer picture of real impact. Landed’s down payment program has not only had a material impact on the lives of our educators, but that of the lives of the students and communities they serve.
Homes Purchased: The purchase price for each home using the Landed down payment program in a given quarter home was aggregated to calculate the top value of homes purchased.
Wealth Built: The average of three Automated Valuation Models (AVMs) were taken to approximate the current value of each of the homes purchased by Landed down payment program customers (HouseCanary, Redfin, Zillow). The difference between the purchase price and the current estimated value of the home, minus the expected amount owed to Landed, plus the principal paid off (which in most cases was minimal given that most assets were originated in the last 18 months) was calculated as the wealth gained.
Customer Employment: Detailed customer employment data are self-reported via post-transaction closing survey.
Student Demographics: Data are collected at the K-12 public school district level from the U.S. Department of Education Office for Civil Rights Data Collection website . District-level data are then weighted according to the number of Landed Down Payment Program (DPP) transactions involving employees from each district. Private and higher education transactions are omitted.
Customer Demographics: Customer and co-buyer race and ethnicity data are self-reported via post-transaction closing survey. Household race and ethnicity is mapped generally in accordance with the approach used to determine the derived_race and derived_ethnicity fields under the Home Mortgage Disclosure Act .
Mortgage origination race and ethnicity data are collected at the Metropolitan Statistical Area (MSA)/Metropolitan Division (MD) level from the Federal Financial Institutions Examination Council's Home Mortgage Disclosure Act website and are based on loans originated during 2019 using the derived_race and derived_ethnicity fields. The following MSAs/MDs are captured: 19740 - Denver-Aurora, CO; 31084 - Los Angeles-Long Beach, CA; 41884 - San Jose-San Francisco-Oakland, CA; 36084 - San Jose-San Francisco-Oakland, CA; 41940 - San Jose-San Francisco-Oakland, CA; 42100 - San Jose-San Francisco-Oakland, CA; 42644 - Seattle-Tacoma, WA. MDs 41884 and 36084 are combined to proxy the San Francisco-Oakland-Hayward, CA MSA, then MSA/MD data are weighted by the number of Landed DPP transactions involving properties in the corresponding MSAs.
Census race and ethnicity data are collected from the 2018 American Community Survey via the U.S. Census Bureau website and are based on total population. The following MSAs are captured: Denver-Aurora-Lakewood, CO; Los Angeles-Long Beach-Anaheim, CA; San Francisco-Oakland-Hayward, CA; San Jose-Sunnyvale-Santa Clara, CA; Santa Cruz-Watsonville, CA; Seattle-Tacoma-Bellevue, WA. MSA data are weighted by the number of Landed DPP transactions involving properties in those MSAs.
Customer Income: Customer household income data are collected from Fannie Mae Form 1008 where available and from customer applications otherwise. Properties are mapped to the corresponding MSA or U.S. Department of Housing and Urban Development Fair Market Rent (FMR) Area. This information is combined with self-reported household size data from Landed’s post-transaction closing survey to identify corresponding 80% and 120% Area Median Income (AMI) levels. 2020 AMI levels are collected from the U.S. Department of Housing and Urban Development website and are based on Section 8 Income Limits (80%) and the Neighborhood Stabilization Program (120%).
Mortgage origination household income data are collected at the Metropolitan Statistical Area (MSA)/Metropolitan Division (MD) level from the Federal Financial Institutions Examination Council's Home Mortgage Disclosure Act website and are based on loans originated during 2019. The following MSAs/MDs are captured: 19740 - Denver-Aurora, CO; 31084 - Los Angeles-Long Beach, CA; 41884 - San Jose-San Francisco-Oakland, CA; 36084 - San Jose-San Francisco-Oakland, CA; 41940 - San Jose-San Francisco-Oakland, CA; 42100 - San Jose-San Francisco-Oakland, CA; 42644 - Seattle-Tacoma, WA. MDs 41884 and 36084 are combined to proxy the San Francisco-Oakland-Hayward, CA MSA, then MSA/MD data are weighted by the number of Landed DPP transactions involving properties in the corresponding MSAs.
Commute Time: Customer commute time data are collected from Google Maps and directions are calculated using addresses of properties purchased using Landed’s DPP as the starting points and associated employer names or addresses as the destinations. Data were captured at approximately 7:00 AM PDT on July 9th, 2020 using the duration and distance fields.
Census commute data are collected from the 2018 American Community Survey via the U.S. Census Bureau website at the employer county level. Data are aggregated from the following response buckets: Less than 5 minutes; 5 to 9 minutes; 10 to 14 minutes; 15 to 19 minutes; 20 to 24 minutes; 25 to 29 minutes; 30 to 34 minutes; 35 to 39 minutes; 40 to 44 minutes; 45 to 59 minutes; 60 to 89 minutes; 90 or more minutes. County-level data are weighted according to the number of Landed DPP transactions involving employees working in each county for which commute times are available.
Commute hours saved and carbon emissions eliminated are calculated as follows. Average commute times for each county are calculated by weighting responses in the buckets above using the midpoint for each. Minutes saved are calculated by comparing customer commute times to these averages. Commute hours assume a two-way commute for 180 workdays per year. Average speeds are calculated as the ratio of distance to duration and are multiplied by minutes saved to calculate miles saved. This information is combined with the fact that the average passenger vehicle emits about 404 grams of carbon dioxide per mile from the U.S. Environmental Protection Agency website to calculate emission reductions.