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Speed 2.0: Evaluating access to universal digital highways

Many governments are committed to promoting the roll-out of fibre optic broadband networks. Little is known, however, about the economic value of these investments. Using house prices to infer the value of high-speed internet access, this column discusses the impact of fibre optic broadband in England. A fast broadband connection is found to increase house prices sufficiently in urban areas to pass a cost-benefit test, but the case for universal delivery in rural areas is not as strong. And even in areas where people are willing to pay for a faster connection, internet providers may be unwilling to deliver it only to see house-sellers appropriate the rent from their investments.

Fast broadband has arguably become one of the critical infrastructures in the 21th century. The possibilities that come with faster internet access are countless – video streaming, e-commerce, or telecommuting, to name just a few. In a recent bestseller, Michael Lewis (2014) argues that superfast connections have even been used by high-frequency traders to rig the US equity market. In the last decades, the break-up of incumbent monopolies have allowed internet service providers (ISPs) such as telecom and cable operators to freely supply their services. Policymakers have traditionally limited their interventions to a few targeted rural areas, but this attitude has recently changed. In the US, the Federal Communications Commission launched the National Broadband Plan in 2010 to provide 100 million American households with access to 100 Mbit/s connections by 2020. In Europe, broadband is one of the pillars of Europe 2020, a ten-year strategy proposed by the European Commission. Its Digital Agenda identifies targets that are as aspiring as those of the US – also by 2020, every European citizen will be able to connect at at least 30 Mbit/s.

A cost-benefit analysis of fast internet connection: New evidence

In order to assess if these broadband plans pass a cost-benefit test, one needs to know the demand for fast broadband, which is a missing key element in the literature. Recently, Nevo et al. (2015) made an important step in this direction by employing high-frequency broadband usage data from one internet service provider in the US. In a paper (Ahlfeldt et al. 2016) we follow a different approach, grounded in the literature that has explored capitalisation effects of local characteristics (see Oates 1969). We argue that it is possible to infer the value brought by a faster internet connection via changes in property prices. Fixed broadband comes bundled with a property whose price might, therefore, be affected. Anecdotal evidence makes a strong case that broadband access is an important determinant of house prices too. In 2012, The Daily Telegraph reported the results of a survey showing that a fast connection can add 5% to a property’s value and that one in ten potential buyers rejects a potential new home because of a poor connection. Rightmove, one of the main online real estate portals in the UK, rolled out a new service to enable house hunters to discover the broadband speed available at any property listed on the site, along with more typical neighbourhood information such as transport facilities and schools.

Providing reliable estimates for the valuation of broadband speed can be tricky.

  • First, we need to separate the effect of high broadband speed on property prices from other favourable locational characteristics (transport access or schools).
  • Second, the available speed is endogenous to factors that determine broadband demand and are likely correlated with property prices, such as high levels of income and education levels.

To account for these effects, we look into the structure of broadband networks. Every property is connected to one and only one broadband delivery point, called a local exchange (LE) in the UK (and called a central office in the US). Essentially, each local exchange supplies a small area and we can identify perfectly all properties that are connected to it. Next, we compute the distance between each property and the exchange, which is the key determinant of broadband speed. Last, we monitor the technology upgrades of each local exchange that change over time. This provides us with an ideal variation of speed over time within an extremely small area. We identify the causal effect of speed on property prices from two sources of variation. First, we exploit a discontinuity across local exchange boundaries over time. In this setting, we compare the house prices of two properties located next to each other that are identical in terms of characteristics but for the speed available to each one of them. Second, we use variation over time within local exchanges. In this setting, we control for all shocks that simultaneously affect prices and technology upgrade decisions, thus the conditional variation in speed is plausibly exogenous. We provide an example in Figure 1 where we show the location of the local exchange, its boundaries, and outline the two approaches used in the identification. Both identification strategies result in very similar estimates.

Figure 1. Distribution of properties and local exchange catchment areas

Figure 1. Distribution of properties and local exchange catchment areas

Notes: The black dots are the locations of local exchanges (LEs), the central nodes serving households within their catchment areas (black boundaries). Black icons denote groups of properties within 200m of a boundary segment separating to LE catchment areas. The coloured dots are property transactions in our data set.

We match data for broadband development with transactions of residential properties for the whole of England over a rather long period (1995-2010).

  • We find an elasticity of property prices with respect to speed of about 3% at the mean of the internet speed distribution, and the increase in value is greater when starting from relatively slow connections.
  • The average property price increased by 2.8% when going from a slow dial-up connection to the first generation of ADSL with speeds up to 8 Mbit/s.

The price increased by an additional 1% when a newer technology ADSL2+ (up to 24 Mbit/s) was launched. This effect corresponds to an increase in school quality by about one third of a standard deviation (Gibbons et al. 2013) or a reduction in distance to the nearest London Underground station of about one third of a kilometre (Gibbons and Machin 2005). The magnitude of the effect is smaller than, for example, the negative effect of having a convicted sex offender living nearby (4%, see Linden and Rockoff 2008) or the positive effect of a good grade awarded to the local school in a school quality review (8.7%, Figlio and Lucas 2004), but more sizable than the effect of the clean-up of a hazardous waste site (Greenstone and Gallagher 2008).

It turns out, however, that the gains are very heterogeneous, and they are highest among the richest people living in the most densely populated area. This variation in willingness to pay for broadband services by English regions is illustrated in Figure 2.

Figure 2. Willingness to pay for speed, by region

Figure 2. Willingness to pay for speed, by region

Notes: The left panel shows the marginal speed capitalisation effects by regions as a fraction of property value. The right panel computes the corresponding monthly monetary rent. Grey solid lines show the respective marginal effects estimated from the regional samples. Black solid lines illustrate the marginal effect for the entire sample. The red vertical line indicates the 95th percentile in the (post-2000) speed distribution across the country.

Evaluating the benefits of the EU’s digital targets

Using these findings, we then evaluate the benefits of the EU’s digital targets for different regions in England, which we compare with available cost estimates. We find that increasing speed and connecting unserved households passes a cost-benefit test in urban areas, while the case for universal delivery in rural areas is not very strong. Still, our results suggest that private provision alone may not be able to supply fast enough connections to a large fraction of the population across the country, but this is only true – perhaps surprisingly – in some urban areas.

Why do internet providers supply sub-optimal speed in those areas where there seems to be a willingness to pay that is in excess of costs? The reason is that the broadband rent goes to the ‘wrong’ economic agent. The broadband speed rent is, in fact, appropriated by property sellers, not by the internet providers. The service providers supply broadband according to supply and demand conditions in the broadband market, which is largely a competitive one. But these conditions do not necessarily reflect the scarcity rents that exist in the property market.

An implication of our results is that there may be a coordination problem among sellers and landlords in the undersupplied areas that pass the cost-benefit tests, perhaps because they are unaware or, most likely, because of their fragmentation. While it would be collectively rational for these sellers and landlords to get together and pay some of the internet service providers’ delivery costs of upgraded technologies—as, then, their properties would become more valuable—free-riding problems make this scenario unlikely. As with other infrastructures, the coordination problem therefore rationalises the public delivery of broadband to undersupplied areas in combination with levies charged to sellers and landlords to recover part of the costs. The political economy of the housing markets literature (Ahlfeldt et al. 2014, Dehring et al. 2008, Fischel 2001, Oates 1969) suggests that homeowners and landlords would support such initiatives as long as the anticipated capitalisation gain exceeds the infrastructure levy.


Ahlfeldt G M, P Koutroumpis and T Valletti (2016), “Speed 2.0: Evaluating access to universal digital highways”, CEPR Discussion Paper 11046.

Ahlfeldt G M, K Moeller, S Waights, and N Wendland (2014), “Game of zones: The political economy of conservation areas”, CESifo Working Paper No. 4755.

Dehring C A, C A Depken, and  M R Ward (2008), “A direct test of the homevoter hypothesis”, Journal of Urban Economics, 64(1), 155-170.

Gibbons, S, and S Machin (2005), “Valuing rail access using transport innovations”, Journal of Urban Economics, 57(1), 148-169.

Gibbons S, S Machin, and O Silva (2013), “Valuing school quality using boundary discontinuities”, Journal of Urban Economics, 75(0), 15-28.

Greenstone, M and J Gallagher (2008), “Does Hazardous Waste Matter? Evidence from the Housing Market and the Superfund Program”, The Quarterly Journal of Economics, 123(3), 951-1003.

Figlio D N, and M E Lucas (2004), “What's in a Grade? School Report Cards and the Housing Market”, American Economic Review, 94(3), 591-604.

Fischel, F A (2001), The Homevoter Hypothesis (Vol. Cambridge, MA.): Harvard University Press.

Lewis, M (2014), Flash Boys, New York: W.W. Norton.

Linden, L, and J E Rockoff (2008), “Estimates of the Impact of Crime Risk on Property Values from Megan's Laws”,  American Economic Review, 98(3), 1103-1127.

Nevo, A, J Turner, and  J Williams (2015), “Usage-Based Pricing and Demand for Residential Broadband”, Econometrica, forthcoming.

Oates, W E (1969), “The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis”, Journal of Political Economy, 77(6), 957-971.

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