Transformational effects on communities are often not to be found in direct government interventions or funding. In dealing with massified systems such as schools or population growth, there is just not enough public money available. For this reason, educational economists have looked to unpack the market links between guardian/ parent choice, schools and their benefits to the broader community. How, for instance, can the presence and functioning of different kinds of schools influence well-being or population behaviour by affecting the engines of choice?
In her survey of the literature, Lauren Taylor notes that, beyond the appeal factors of location, sticker and operating costs, and house design, proximity to amenities, neighborhood quality, and school quality are all reflected in a house’s retail price. In Australia, any new area will eventually have a state school provision – but the supply of educational services here (as opposed to other places in the world) is highly variegated. David Hastie, Associate Dean for Education Development at Alphacrucis, notes that “Australia has the fifth highest school choice in the world”, after countries such as the Netherlands and Chile (sometimes for unrepeatable local historical reasons.)
Taylor notes that: ‘Of particular interest to homeowners, economists, and policy makers is the effect of school quality on housing prices in any given area.’ This depends, of course, on how one defines ‘quality’. Measuring “school quality is often difficult and very subjective”. She distinguishes between studies based on “output-based” means of measurement (standardized test scores, school ranking, etc) as opposed to “input-based” measurements (teacher-pupil ratio, per-pupil spending) compared to house prices in a defined region. Clark & Herrin (2000) note that both input and output measures are important. "However, elasticity estimates of input measures tend to be higher than those of output measures, with the average class size by far the strongest influence. There is some evidence to suggest that the benefits of additional teachers likely outweigh the costs." Taylor notes attempts by economists such as Tiebout (1956), who predict house choice (and so population movements toward homogeneity) in the basis of preference patterns for local public goods (including schools, parks, and other amenities). ‘According to such a model, individuals with similar preferences will populate a community’, on the basis of the average, shared measure of perceived Local Public Good. Such economic logic is particularly suited to advanced consumerist societies such as the USA, where choice is conceivably less restrained by embedded ‘thick cultures’. Livy notes that it is also important to distinguish when the buyer is making a choice: "standardized test scores positively drive this relationship during the housing market decline in the latter half of the period from 2007 to 2012; in contrast, there is no overall relationship between school quality and appreciation rates during the preceding period of housing price increases from 2000 to 2006." (Livy 2017)
Three studies based on output scales find positive relationships between school quality and house prices. A 2000 survey (based on self-reporting) ‘found that the presence of good schools was selected as the third most important neighborhood characteristic for both buyers and sellers between ages 25 and 44 with children.’ The Reinvestment Fund (TRF) replicated this by geocoding residential sales between 2006 and 2007 with linked percent of elementary school students scoring proficient or above on the combined Pennsylvania System of School Assessment (PSSA) for Reading and Math at the schools in that zone. The study discovered that ‘for every level of school quality improvement, the housing price increases 0.52 cents per square foot on average. For a 900 square foot home, a 10 point increase in school quality translates into a $4,500 increase in sales price.’  Disincentives included the impact of new construction and neighborhood disinvestment (-$1.50 per square foot) and Crime scores (-$1.00 per square foot). Owusu-Edusei and Molley Espey’s 2003 study in Greenville, South Carolina used school rankings related to a ‘hedonic’ classification of houses (ie. their amenity) and found that, 1) high-ranked schools have values embedded in single-family housing prices and 2) greater commuting distances to schools has a negative impact on the value of property. Proximity to and quality of a school does affect the prices of housing in its respective school district: houses with elementary schools within 2640 feet (a half of a mile) of their properties have prices 18% higher than those of houses located further than 10560 feet (2 miles) from an elementary school. Houses with middle schools within 10560 feet of their properties have prices 16% higher than those of houses located further than 10560 feet from a middle school and houses with high schools within 10560 feet of their properties have prices 12% higher than those of houses located further than 10560 feet from a high school. Quality had a marginal price effect: where elementary schools were rated Good, houses sold at 12% higher than those in districts with schools with a worse rating; while a middle school rated Average bolstered house prices by 31% compared with a school of a worse rating.
Controlling for variation in neighborhood characteristics, property taxes, and school spending, Black (1999) found similar patterns in correlations between single-family residences across 39 school districts outside of Boston and test scores on the Massachusetts Educational Assessment Program.
Using North Carolina standardized school quality scores, Wulsin notes “that when families buy a home, they also buy the right for their kids to attend the local public school in that district and that the price of that right is incorporated into the price of the house they purchase”. In Durham County, factors included the fair market value of the house, observable characteristics that affect house prices, which school attendance zone the house is in, and the distance the house is to the border of the school attendance zone. Wulsin concluded that “parents do pay more to live in areas with better schools”: a 10% increase in elementary school scores leads to an 11% increase in housing prices, a 10% increase in middle school scores leads to an 11% increase in housing prices, and a 10% increase in high school scores leads to a 5% increase in housing prices. This trend has been observed across the United States. Since better quality schools increase the real estate value of houses in their areas, improving schools can be a method for improving neighborhoods and stimulating economic growth.
Chung notes that the related inverse is also true: if school choice is deregulated (as is the case when, for instance, a Christian school is placed in a new suburban area, providing choice for students locked into mandatory school drawing areas for local state schools, people from high performing, expensive housing areas will flow towards areas where they can get the same quality of educational access options at a lower housing cost. "School choice reform", says Chung, "weakens the link between residential locations and school options by offering more options for schools students can attend. With the introduction of the school choice reform, consensus bidding theory posits that if people in low-performing school districts are able to access high-performing schools without an increased cost of housing, people in high-performing school districts or schools have the incentive to move to a community with low-housing prices at no cost to the school quality. Thus, this leads to an increase in housing prices in lowperforming school districts and a decrease of housing prices in highperforming school districts."
 The Reinvestment Fund, ‘Schools in the Neighborhood: Are Housing Prices Affected by School Quality?’, Reinvestment Brief no. 6, https://www.reinvestment.com/wp-content/uploads/2015/12/Schools_Quality_and_Housing-Brief_2009.pdf, accessed 28 March 2019.
 The Reinvestment Fund, ‘Schools in the Neighborhood’.
 “Instead of the traditional hedonic price function, Black used the formula ln(priceiab) = α + X’iabβ + K’bφ + γtesta + εiab in which boundary dummies (the K term) account for unobserved characteristics shared by houses on either side of the attendance district boundary.” (Taylor)