The Effect of Impact Fees on the Price of New Single-family Housing

Abstract

This paper provides new evidence on the effects of impact fees on housing prices, using an inventory of single-family housing sale transactions in the 38 cities and towns within King County, Washington, for the period 1991-2000. Although the effect of impact fees on housing prices has been examined previously, earlier studies have been limited by methodological deficiencies. This paper examines the effect of impact fees on new housing and their differential effect on housing price based on the quality of housing, and finds that the effect of impact fees on the price of new housing is quite significant and elastic, raising new home prices by about 166 per cent of the amount of the fee. The increase is 358 per cent for the higher-quality homes and is statistically insignificant for the lower-quality homes.

The link between infrastructure charges and housing affordability in Australia: where is the empirical evidence?

“…Evans-Cowley and Lawhon (2003) also note that developers do not absorb these costs by way of lower profits, as the market (through the land acquisition process) has already set the cost structure at which development is encouraged (passing back). Other research also confirms the premise that developers are likely to make investment decisions based on profitability and if that profitability is reduced in one area, it will move its operations to another to maximise profits (Burge 2008;EvansCowley and Lawhon 2003;Mathur,Waddell, and Blanco 2004). Watkins (1999) offers the only perverse theory, claiming that exactly half of any charge is absorbed by the developer by way of reduced profits, with the other half either passed on or passed back.…”

“…As discussed above, if neither the developer nor landowner is willing to bear the cost of these additional supply chain costs, then the burden must fall to the home buyer by way of higher house prices. This concept is consistently captured by a vast number of academics, particularly in the USA and Canada over the past three decades including (but not limited to): Ellickson (1977), Snyder and Stegman (1986), Downing and McCaleb (1987), Huffman et al (1988), Smith (1989a, 1989b), Singell and Lillydahl (1990), Skaburskis and Qadeer (1992), Altshuler and Gomez-Ibanez (1993), Dresch and Sheffrin (1997), Brueckner (1997), Skidmore and Peddle (1998), Yinger (1998), Baden and Coursey (1999), Mayer and Sommerville (2000), Evans-Cowley and Lawhon (2003), Ihlanfeldt and Shaughnessey (2004), Mathur, Waddell, and Blanco (2004), Campbell (2004), Been (2005), Evans-Cowley, Forgey, and Rutherford (2005), Burge and Ihlanfeldt (2006) and Evans-Cowley et al (2009).…”

“…Mathur, Waddell, and Blanco (2004) chose earlier work by Altshuler and Gomez-Ibanez from 1993 in stating that under tight market conditions … we would expect the fees to be passed on principally to consumers, while developers and land-owners would absorb most of the cost of the fees in the form of lower profits under soft market conditions. This is the overwhelming conclusion of the majority of the contemporary literature.…”

“…These include studies on: the price impacts of infrastructure charges on new and existing housing by Smith (1989a, 1989b), Singell and Lillydahl (1990), Lawhon (1996), Dresch and Sheffrin (1997), Baden and Coursey (1999), Mathur, Waddell, and Blanco (2004), Ihlanfeldt and Shaughnessy (2004), Campbell (2004), Evans-Cowley et al (2009), Burge and Ihlanfeldt (2006); on existing homes only (Mathur 2007); on the price of undeveloped land (Nelson et al 1991;Nelson, Frank, and Nicholas 1992;Ihlanfeldt and Shaughnessy 2004;Campbell 2004;Evans-Cowley, Forgey, and Rutherford 2005); on vacant developed residential lots (Skaburskis and Qadeer 1992;Evans-Cowley, Forgey, and Rutherford 2005); and on the supply of new housing (Skidmore and Peddle 1998;Mayer and Somerville 2000). Each of these studies used various forms of hedonic modelling, an econometric technique for multiple regression analysis.…”

An integrated approach to evaluating urban forest functionality

“…Lower scores represent more urban areas, higher scores represent less developed areas with larger patches of forest and lower population densities (values in parentheses are eigenvectors) October 2001. This includes records of all sales within the County as well as the parcel and improvement characteristics relevant for tax appraisals, and has been used in many hedonic models for the local housing market (Franklin and Waddell 2003;Mathur et al 2004;Cunningham 2006). We also incorporated an entropy measure of morning peak-hour transportation accessibility for single-occupancy vehicles to commercial employment, calculated using data from the Puget Sound Regional Council travel demand model (Franklin and Waddell 2003).…”