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Quality of life may be a subjective concept, but the lifestyle preferences of homeowners and homebuyers ultimately create empirical measures: The attractions and amenities that appeal to us create a price premium as we compete for finite housing options convenient to them. In 2024, we explored this dynamic in the walkable, often pre-war neighborhoods around Indiana’s urban centers, and found a notable increase in demand leading to faster price appreciation (even from a much more affordable baseline in many cases) in these areas.
In this sense, walkability is often about density in our built environment. But what about natural amenities and the man-made developments meant to help us explore and enjoy the outdoors?
Indiana boasts hundreds of lakes and has completed more than a hundred miles of new trails in the last seven years. Waterfront development has been a focus of regional planning along the White River in Central Indiana and communities along the Ohio River at the state’s southern border.
Indiana also has more than 2,500 parks, from the sprawling 16,000 acres of Brown County State Park to local neighborhood pocket parks; a recent analysis from the IU Polis Center found that less than one in three Hoosiers live within a half-mile of one of them.
But what price are Indiana homeowners willing to pay for proximity to these outdoor features that connect them to nature “beyond their backyards?” And what insights can spatial analysis of these assets with transactional housing data provide to real estate professionals?
Homebuyers pay a premium to live near trails, parks, and water; moreover, the impact on property value gets stronger—and faster—as properties are closer to the amenity. We analyzed 10,000 home sales from 2021 to 2023 to measure the effect of these amenities, controlling for other factors (size of the home, age of the home, and ZIP Code).
We find that lakes have the strongest effect on prices. A home within 60 feet of a large lake (at least 500 acres) is worth 81% more than the same home if it were 1,000 feet from the lake. The price premium is 27% for medium lakes (100-499 acres) and minimal for small lakes.
Streams and parks have a strong effect on prices, though weaker than lakes. A 60 feet distance compared to a 1,000-foot distance equates to a price premium of 11% for parks and 12% for streams and rivers.
Trails have a 6% price premium when comparing a home with direct trail access (60 feet) to the same home if it were 1,000 feet away. While this is the weakest price premium, it can vary from trail-to-trail. In fact, we find a 24% premium for home 60 feet from the Monon Trail.
These results are consistent with similar research. In a review of 33 studies, the price premium for being adjacent to a park was generally 8-10%. Locally, IU’s Public Policy Institute found that, in 2019, homes within 250 feet of an Indianapolis park were worth $14,000 more than the county average. Within a quarter-mile of a park, property values grew faster than the county average from 2016-2019 (15% compared to 11% county-wide).
Nearby greenway trails likewise usually add value, though the effect is often more modest. A 2019 synthesis of 20+ trail impact studies found that homes adjacent to recreational trails were typically 3–5% higher in price compared to similar homes away from the trail. However, the impact of trails can be specific to certain trails. A 2003 study on the impact of greenways in Indianapolis found that, while the Monon Trail had a positive impact on nearby property values, other greenways at the time did not. We find similar results across the state—the Monon still shows strong price impacts along with trails in Madison, Indiana, while the Nickel Plate Trail, Cardinal Greenway, and Fall Creek Trail have no statistical effect.
We created a statistical model to measure how much distance to four amenities—parks, trails, lakes, and streams—impacted home prices. To isolate the effect of these amenities, we included square footage, age, and ZIP Code in our model. This means we “controlled” for characteristics of the home and the neighborhood. While there are more detailed ways to measure home and neighborhood factors, our model consistently explained 67% of the price difference from one home to another, showing that is is robust and accurate.
Distance matters exponentially more as you get closer and closer to an amenity—being able to walk to a lake is nice, but having lake views or being on the shoreline is worth much more. To account for this, we built a model that separately measures affects within 1,000 feet and beyond 1,000 feet. Even within those categories, price impacts are still exponential—prices rise faster and faster as you get near a park for example, so we reflect the price premium every time you cut the distance to an amenity in half.
Medium and large lakes have the strongest effect on prices. The impact of a lake depends on its size, so we measured small (less than 100 acres), medium (100-499 acres), and large lakes (500 acres or more). As examples, Lake Wawasee, Indiana’s largest natural lake, is over 2,000 acres, while Eagle Creek Reservoir is 1,400 acres.
Bartholomew County’s Grandview Lake is about 400 acres. For homes within 1,000 feet of a large lake, value rise 15-20% each time the distance to the lake is halved. All else being equal, a home 500 feet from a large lake is worth 16% more than an equivalent home 1,000 feet away. Compared to 1,000 feet, the price premium is 35% at 250 feet, 56% at 125 feet, and 81% at about 60 feet, which, for many properties, would be adjacent to the lake.
For example, homes near Geist Reservoir often sell for two to ten times the county-average sale price, and the adjacent ZIP Codes are among the most expensive in the state. One home only 65 feet from Morse Reservoir sold for $266 per square foot, while a home 200 feet away on the same road sold for only $163 per square foot.
Medium lakes have about one third of the effect on prices that large lakes have. For homes within 1,000 feet, prices increase 7% each time the distance is halved. A home is worth 27% more if it is 60 feet from a medium-sized lake compared to 1,000 feet.
Beyond 1,000 feet, large lakes still have some positive influence on prices, while medium lakes do not. Small lakes have no statistical effect on home values statewide, though in some counties they do have an effect.
These graphics show how being closer to an amenity can increase a home’s value—even when the home’s size, age, and neighborhood stay the same. For this example, we imagined a home worth $250,000 located 1,000 feet from the amenity.
We consider streams and rivers together, and they have a positive impact on home prices. For homes within 1,000 feet, value increase 3% every time that distance is halved. That means, compared to a home 1,000 feet away from a stream or river, value will 3% higher at 500 feet, 6% higher at 250 feet, 9% higher at 125 feet, and 12% at about 60 feet (likely adjacent to the water).
Beyond 1,000 feet, home values tend to decrease slightly when closer to a river. This may reflect the fact that rivers tend to run through older, more industrial parts of cities, but the effect is very small even if it is statistically significant.
Parks have a similar effect to streams and rivers: 3% increase in value every time you cut the distance to a park in half. Compared to a home 1,000 feet from a park, value will be 3% higher at 500 feet, 5% higher at 250 feet, 8% higher at 125 feet, and 11% higher at about 60 feet.
Homes within 1,000 feet of a trail have a 1.5% price premium every time that distance is halved, meaning value rises by 3% at 250 feet from a trail and 6% at about 60 feet from a trail. However, this effect varies from trail to trail, which we discuss in the next section.
Indiana’s largest natural lake (Lake Wawasee) is located in Kosciusko County, and it has stronger price effects than the statewide average. A home 60 feet from the lake is worth 124% more than the same home 1,000 feet from the lake. The effect of medium and small lakes is also much stronger in the county; the area’s 100+ natural lakes are a collective selling point, so it is likely that properties around smaller bodies of water benefit from a halo effect bestowed by the unique concentration.
Even small lakes in Kosciusko County add a price bump, and large lakes more than double home prices.
This county’s largest lake is Grandview, which falls in the “medium lake” category. Home values double between 1,000 feet from the lake and 60 feet from the lake. The effect continues beyond 1,000 feet, with a positive price impact that is about as strong as the statewide impact of trails. No homes are in Bartholomew County are within 1,000 feet of a large lake, but even beyond 1,000 feet, large lakes have a positive impact on home prices in Bartholomew County. This may indicate increased value as homes are closer to Lake Monroe and the higher property values of Brown County.
Small lakes boost prices in Bartholomew County and homes next to medium lakes are double the price of an equivalent home 1,000 feet away.
In Madison, trails have a very strong price premium. The trails in the area include the Riverfront Trails as well as the network of hiking trails in Clifty Falls State Park. It may be proximity to the park itself that drives these prices up. Rivers and streams do not show a positive price effect in this ZIP Code, though that could be because the older part of town was built closest to the river. Though it is historic and charming, it is also generally lower-valued than the outlying subdivisions. However, looking at median sale price trends in this ZIP (47250) and the township (Madison) that includes the downtown district is suggestive of the success of redevelopment strategies capitalizing on adjacency to the Ohio River: Five-year price appreciation for the ZIP (64%) and township (62%) run well ahead of Jefferson County as a whole (55%).
Trails in Madison boost prices, while other amenities did not have a measurable effect on prices.
This trail shows a very high price premium equivalent to the impact of a medium-sized lake. Quite literally, properties along the Monon benefit from a beachfront-scale price bump. A home about 60 feet from the trail is worth 24% than one 1,000 feet from the trail. In this analysis, we only considered home sales in Hamilton and Marion counties and within one mile of the Monon. We tested the Cardinal Greenway, the Nickel Plate Trail, and the Fall Creek Trail, but found no price effect. Those who are familiar with Central Indiana, however, may recognize that home values in areas around completed portions of the Nickel Plate (in Hamilton County) and Fall Creek (particularly the neighborhoods around 38th Street) were being influenced by other commercial and public infrastructure investments during the study period, making it difficult to parse trail effects.
The Monon Trail has the same price impact as beachfront property on a medium-sized lake.
In Porter, Lake, and La Porte Counties, four amenities have a positive relationship to price: parks, streams, medium lakes, and large lakes. In this area, large lakes are obviously dominated by Lake Michigan, and the price premium for large lakes is lower than the state average. While some areas have very desirable lakefront property, in other communities, being close to the lake means being next to industrial sites. Medium-sized lakes, however, command an above average premium.
Parks and streams have more of a price impact here than in the state, and medium lakes offer the largest price premium.
In Brown County, lakes command an even stronger price premium than the state average. Trails and parks, while plentiful in the county, do not show a clear positive relationship with prices—though their presence in the county is certainly a desirable amenity for many residents. Brown County’s medium-sized lakes are Yellowwood Lake (123 acres) and Sweetwater Lake (279 acres). A property on the shore of one of these lakes is estimated to sell for three times the price of an equivalent propety 1,000 feet away (213% price premium). Estimated home price increases by 30-50% each time you halve the distance to the lake. Nearby large lakes include Lake Lemon and Lake Monroe (in Monroe County). These command a premium on par with the state average.
Medium lakes have the strongest impact in Brown County. While trails and parks do not have a hyperlocal price effect, they likely support county-wide values.
Proximity to natural amenities—particularly lakes, trails, parks, and streams—has a measurable and often substantial impact on home values in Indiana. Among all amenities studied, large lakes consistently generate the highest price premiums, with the value of homes rising significantly as distance to the lake decreases. Even medium-sized lakes show strong, positive effects on price within 1,000 feet. Trails, streams, and parks also enhance property values, though the magnitude varies, especially in the case of trails where specific corridors like the Monon Trail exhibit unusually high premiums.
These effects are most pronounced within close range—typically within 1,000 feet—and grow exponentially stronger as distance is halved. This underscores the value of direct access and views. Furthermore, localized analyses confirm that these statewide trends hold true—or are even amplified—in amenity-rich counties like Kosciusko, Bartholomew, and areas along the Monon Trail.
For buyers and REALTORS® alike, understanding these spatial dynamics can support more informed decisions around pricing, marketing, and home selection.
For listing brokers, these findings may affirm the pricing strategies they have already formulated based on their knowledge of local markets and neighborhood demand. But these average price premiums will provide context and additional insight for working with homeowners whose properties are adjacent to these amenities.
For buyers and their brokers, this study may help set expectations when looking at homes that meet their lifestyle and recreational preferences – how much is direct waterfront access worth in the context of their budget relative to square footage and other property attributes, for example, versus expanding their search to homes with shared access?
For policymakers, developers and community planners, we hope this analysis adds to the body of research showing the impact of green space, trail and park proximity in new residential development in homebuyer demand and talent attraction.
Indiana has embarked on its largest single infusion of state funding for trail development over the past eight years ($180 million in the Next Level Trails program). Through two rounds of the state’s billion-dollar READI grant program, forty regional trail and park projects received funding.
Available consumer preference surveys also show growing affinity for these amenities among real estate consumers. In the MIBOR 2022 Community Preference Survey (covering a region accounting for roughly 40% of Indiana’s annual residential transactions on average), 65% of respondents ranked the availability of parks, trails and playgrounds nearby as important or very important to their quality of life, up from 60% in 2018.
However, the number of respondents satisfied with this metric fluctuated—from 58% in 2012 to 65% in 2018, but falling to 57% in 2022. As the state’s park and trail system generally expanded over this time period, these results could suggest (counterintuitively) that Hoosiers moved to areas with less access. It’s more likely that current and prospective homeowners found greater value in outdoor experiences – especially through the pandemic—and lower satisfaction signals a demand for even greater access. Meeting this demand could bolster local real estate markets.
Finally, this analysis is also relevant to homestead property tax assessments, especially for homeowners whose properties are near—but not immediately adjacent to—significant lakes, parks or trails.
Mass assessment techniques use a sample of nearby comparable sales to adjust property values to reflect the market; homes that are directly on the waterfront, for example, are likely to boast higher values but may also see faster price appreciation. Homeowners whose properties are not immediately adjacent should be watchful that their assessments aren’t trended according to home sales involving neighbors with more appealing frontage.
To quantify the impact of proximity to natural amenities on housing prices, we estimated a series of hedonic regression models using residential sales data from Indiana. The base model is a log-linear specification, with the dependent variable being the natural logarithm of sale price. This allows the interpretation of coefficients on log-transformed distance variables as approximate percentage changes in price.
Distance to lakes, trails, parks, and streams were calculated using the Euclidean distance from each home to the nearest feature of each type. For each amenity, we split distance into two components:
This two-part structure captures diminishing effects with distance, allowing sharper estimates of close-range amenity impacts. All distance variables were log-transformed after adding 1 to avoid log(0).
Models control for building size, age, and ZIP code fixed effects to account for neighborhood-level pricing differences. ZIP codes act as a proxy for unobserved neighborhood quality, school district boundaries, and other localized amenities. Robust standard errors (using the sandwich package in R) were applied to address heteroscedasticity, which is typical in cross-sectional housing data.
For lakes, I further stratified the analysis by lake size. Lakes were grouped into three categories based on surface area:
Each group was tested separately using distance to the nearest lake of that size class. This allows the model to account for heterogeneity in lake size and public perception.
To validate statewide patterns at the local level, we re-estimated models for specific geographies (e.g., Kosciusko County, Bartholomew County, ZIP code 47250). We also tested the effect of proximity to specific trails such as the Monon Trail by filtering trail segments with name-based matching and isolating ZIP codes intersected by the corridor.
All spatial operations were performed using sf, dplyr, and tigris in R. Models were estimated with lm() and diagnostic tests included residual plots and White’s test for heteroscedasticity.
| Variable | Coefficient | Std. Error | Significance |
|---|---|---|---|
| (Intercept) | 14.438 | 0.380 | *** |
| Total Square Feet (per 100 ft²) | 0.0304 | 0.0004 | *** |
| Age of Home | -0.00475 | 0.00015 | *** |
| log(Park Dist ≤ 1000 ft + 1) | -0.0368 | 0.0115 | ** |
| log(Park Dist > 1000 ft + 1) | 0.0127 | 0.00185 | *** |
| log(Trail Dist ≤ 1000 ft + 1) | -0.0212 | 0.0125 | * |
| log(Trail Dist > 1000 ft + 1) | 0.00166 | 0.00186 | |
| log(Stream Dist ≤ 1000 ft + 1) | -0.0403 | 0.0180 | * |
| log(Stream Dist > 1000 ft + 1) | 0.00488 | 0.00237 | * |
| log(Small Lake Dist ≤ 1000 ft + 1) | -0.00666 | 0.00807 | |
| log(Small Lake Dist > 1000 ft + 1) | -0.00279 | 0.00169 | |
| log(Medium Lake Dist ≤ 1000 ft + 1) | -0.0869 | 0.0413 | * |
| log(Medium Lake Dist > 1000 ft + 1) | -0.00220 | 0.00431 | |
| log(Large Lake Dist ≤ 1000 ft + 1) | -0.2151 | 0.0389 | *** |
| log(Large Lake Dist > 1000 ft + 1) | -0.0113 | 0.00646 | |
| ZIP Code Fixed Effects | Included | ||
| R² | 0.67 | ||
| N | 10,000 | ||
Significance: *** p < 0.01, ** p < 0.05, * p < 0.1