Land readjustment is a vital but difficult and time-consuming process: formulating a sort of retroactive version of planning in neighborhoods that developed informally, with unsanctioned dwellings chaotically built in ways that leave some with no access to streets and paths. According to UN-Habitat, 863 million people around the world lived in such settings as of 2014, and the number could rise to 3 billion by 2050. The agreed draft of the New Urban Agenda for the Habitat III conference in Quito, Ecuador, notes that the “rising number of slum and informal settlement dwellers” contributes to intense challenges that exacerbate global poverty and its risks, from a lack of municipal services to increased health threats.

But evolving technology may facilitate revision of these organic layouts in ways that lead to minimal displacement and speed the absorption of such neighborhoods into a city’s formal structure, thus providing residents basic services—from sanitation and drainage systems to access for fire and medical emergencies. One of the more promising tools is Open Reblock, a platform currently in a pilot phase in areas around Cape Town, South Africa, and Mumbai, India. The project stems from a collaboration among Shack/Slum Dwellers International (SDI, is a network of urban-poor communities in 33 countries), the Santa Fe Institute (SFI, a nonprofit research and education organization), and Arizona State University. 

SDI has long been involved in grassroots “reblocking”—essentially another way of characterizing the land-readjustment process. Luís Bettencourt, a professor of complex systems at the Santa Fe Institute, explains that his group, which focuses on “cities as systems,” began working with SDI a few years ago. There was a useful convergence in the high-level, statistics-and-data thinking of the SFI group with the on-the-ground “census” efforts SDI used in its work with informal-settlement communities. 

SDI’s reblocking efforts could be painstaking. Residents led the process of mapping a neighborhood—on paper. Then they gathered at community meetings, arranged cutouts representing every local structure over that map, and began shifting them around to devise new paths and roadways. While this active collaboration was profoundly beneficial, the analog methodology wasn’t exactly speedy. 

Ever-more-accessible digital technology has in recent years eased the process, says SDI Programme Officer: Data Management Anni Beukes. The group now uses a geographic information system (GIS) tool to map settlement boundaries and services available, and then relies on a separate tool for detailed household-level surveys and precise measurements of every structure. Given the wide availability of mobile devices, the process is open to—and indeed dependent on—direct resident participation. 

Enrique R. Silva, research fellow and senior research associate at the Lincoln Institute, notes that similar tech-mapping tools are impacting such efforts around the world. “You can map something almost immediately,” he says, and involve community members in that process. He points to efforts, backed by the Lincoln Institute and others, that rely on “cheap and universal” devices and crowd-sourcing tools to reach similar goals across Latin America.

A master map that is available in digital form also creates new possibilities. Open Reblock is an example. It deploys a custom algorithm to read a digital map of an informal settlement and propose what it sees as the optimal strategy for reblocking it. (The algorithm is written to prioritize existing roadways and structures, echoing the traditional goal of minimizing displacement.) This process takes just minutes, at most. 

“When I first showed it to our communities, they said, ‘You’re taking our paper cutouts away’!” Beukes says with a laugh. They weren’t wrong—and they weren’t actually protesting. (“At least the younger ones weren’t; some older participants,” she adds, “can be hesitant in their uptake of new technology.”)

But what Open Reblock produces is not meant to be a strict directive or an end point—community members can still tweak the results based on their direct knowledge and concerns. Indeed, Open Reblock depends on such participation—“creating a shared reality where people can play and create this future reality,” Bettencourt says. “It’s basically a town-planning tool, at the level of a neighborhood.” And by offering “a proof of principle and a starting point” for negotiations, he adds, it radically speeds up one of the toughest steps in the process. 

Beukes says participants in the pilot programs have reacted with enthusiasm toward the new possibilities of this system. It means that a final plan will exist in a form that city officials can respond to more easily, and it ensures that all parties are considering the same geospatial data and planning scheme. “It’s a template for discussion,” Bettencourt adds, one that “puts everyone literally on the same map.” 

With a grant from Open Ideo, Bettencourt’s team and SDI are working to improve the design of Open Reblock’s interface, with feedback from community participants in Cape Town and Mumbai. The entire project is being created in open-source code (available on Github), both to encourage improvements from anyone who wants to be involved and to make it easier to scale up future versions for widespread use anywhere. 

The project is, of course, not a magic solution. Land readjustment can be contentious, and Silva points out that important issues around the value of any given settlement dweller’s property must still be worked out on a more individual, human level. Bettencourt and Beukes agree that Open Reblock is a supplement to, not a substitute for, existing processes.

Still, Bettencourt points to UN-Habitat numbers to speculate that there may be a million neighborhoods around the world in need of reblocking. “That’s a scary number,” he says. And it adds to the sense among some observers that there’s just something impossible about the effort—particularly when, on a case-by-case basis, the process gets bogged down over time. 

But all this may be less intimidating from a technologist perspective. Think of the mapping and data-collection tools that have emerged in recent years as an early step that builds on the long-existing work of SDI and others. Open Reblock is just one more iteration of that trajectory. “I think we have all the ingredients, but we have to start doing,” Bettencourt says. “If there’s a system to capture the data and run proposals on top of it, that’s a big step. It doesn’t create the change, but it helps.”

Rob Walker (robwalker.net) is a contributor to Design Observer and The New York Times.

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbo

The single greatest challenge to any type of land value taxation system is accurate valuation of land on a large scale. In urban areas where nearly all real estate sales data represent transfers of land with improvements, it is difficult to divide prices between land and building components. Although many jurisdictions require a separate listing of land and building values on their tax rolls, these allocations will not affect the final tax bill if the tax rate is the same on both.

Any special tax on land value alone would increase the need to assign more accurate land values to parcels that have been improved over many years. As a result, skepticism as to the feasibility of this process has proven a major stumbling block to serious consideration of two-rate property taxes and other forms of special land taxation. Many observers have concluded that the practical problems of land assessment prevent the realization of the many theoretical benefits it offers.

New advances in computerized approaches to property assessment have important implications for this debate. While land valuation presents special problems in the analysis of sales data for improved parcels, it also can benefit from location analysis and land value mapping techniques. Buildings can and will vary unpredictably in both type or value from lot to lot, but land values for adjoining or nearby parcels should have a more constant relationship to one another. More than 20 years ago, Oliver Oldman of Harvard Law School, considered the implications of this situation for an appeals process under a land value tax, recognizing that a successful challenge to one parcel’s valuation would have implications for many other assessments as well. He wrote, “The key to developing an accurate land-value assessment roll is the process of land-value mapping.” Now the technology is available to achieve this goal.

In a recent seminar at the Lincoln Institute, representatives of the Auditor’s Office in Lucas County, Ohio, which includes the city of Toledo, joined a group of economists, appraisers, lawyers and local officials to examine current methods of land valuation. Lucas County has one of the most sophisticated appraisal systems in the country, with almost 20 years of experience in using computerized methods of spatial data analysis for property taxation. The seminar provided a valuable opportunity to discuss the county’s innovative approaches to the integration of geographic information systems and computer-assisted land valuation to estimate the effect of location on real estate market value.

Traditional Methods of Land Valuation

There are several standard methods of deriving a value for unimproved land, all extremely problematic as the basis for jurisdiction-wide assessment.

Comparable Sales: The most straightforward method is an analysis of sales of comparable unimproved land, adjusting the prices to account for any differences in size, location, and features. Similarly, the capitalization of rental income for comparable vacant land can serve as a basis for estimating its sale price. However, these methods are difficult to apply in densely populated urban areas where sales or rentals of unimproved land are rare. The pool of sales data can be expanded if sales of improved land are followed soon after by demolition of the buildings. In that case, the unimproved land value can be estimated as the purchase price minus the costs of the demolition. Although such sales provide an important check for estimated values produced by other approaches, they do not exist in sufficient numbers over a varied enough geographic range to serve as the sole basis for assessment.

Income Analysis: The land residual method begins with an estimate of the income yielded by the developed property. The building value is then calculated, and from that the income attributable to the building is derived. Capitalizing the remaining income then provides a value for the land. However, even a cursory description of this method suggests the difficulties of its application. In particular, the existence of depreciation, or any deviation from highest and best use that would distort the income available to the unimproved land, can leave the independent value of the improvements extremely uncertain.

Cost Analysis: Similar problems confront a division of value according to the depreciated reproduction cost of the improvements. This method assumes that structures can be worth no more than their cost of construction, and assigns all remaining value in the improved parcel to the land itself. Physical, economic or functional depreciation greatly complicates the attempt to calculate building value, however, so this method requires fairly new construction whose price can be confidently estimated as a measure of value. The financial effect of various forms of obsolescence can only be measured accurately through examination of sales data, which will almost never be available for the building alone.

Cost of Development: A full-scale market appraisal of potential development alternatives provides another basis for estimating the sale price of unimproved land. This is the approach taken by developers considering new uses for land, land trusts seeking to acquire and preserve undeveloped open space, and taxpayers claiming deductions for charitable contributions of development rights. However, it is most suitable for valuing undeveloped land to be used for residential subdivisions. Even in these situations, it requires extensive study of the potential market for such properties, local restrictions on development, and the physical attributes of the land that would affect its building capacity, such as soil and drainage characteristics. This type of exhaustive individual appraisal is appropriate for purchasers or developers of individual parcels, but is not feasible for annual assessments for all parcels in a taxing jurisdiction.

Other valuation methods, such as derivation of typical ratios of site value to total improved property value, are even less useful in the case of densely developed urban property, where buildings of all sizes, ages and utility may be found in close proximity on fairly similar parcels of land.

New Approaches: CAMA and GIS

The greatest change in assessment practice over the past three decades has involved the use of computers and mathematical formulas to establish a relationship between property characteristics and sale prices, thereby permitting an estimate of the market value of other properties not subject to a recent sale. This approach is known as computer-assisted mass appraisal (CAMA). Site characteristics such as size and location are important elements of these mathematical models, raising the possibility of estimating the effect of location on parcel value.

At the same time, the development of computerized geographic information systems (GIS) has permitted assessors to develop location-based property records or cadastres, and to coordinate sales data with location. More sophisticated and less expensive GIS technology now offers the potential for full integration with CAMA for spatial analysis. Initial attempts to quantify location effects faced difficulties not only in defining and maintaining “economic neighborhoods” or zones, i.e., contiguous areas of relatively homogeneous land values, but also in understanding the dynamics of the interactive, elusive locational factor. Some efforts developed different mathematical models for each geographic region or “cluster” of properties with similar characteristics. However, these approaches could not capture the many complex, interrelated and significant micro-variations within any given neighborhood, and could not reduce the determination of location value to an objective process.

Lucas County pioneered a new approach to location value-the use of GIS tools to develop a response surface that represents the effect of location on land value. The response surface is a fitted three-dimensional surface that represents a percentage adjustment to land and/or land and improvements based on a parcel’s geocoded location. Included in the analysis are geographic coordinates and distances from important features, such as other recent sales, institutions, amenities or other “value influence centers.” This analysis results in a three-dimensional representation, with the height of the surface (z) at any specific x-y coordinate indicating the approximated location value of that parcel. This variable is then evaluated with others, such as land and building size, quality, condition and depreciation, to produce a total estimated value for the parcel.

In the Lucas County example, the response surface differs from a mathematical equation in that it is developed through a spatial analysis process available in GIS to estimate the effects of location on value and refine those estimates after comparing them with sales and appraisal data. This approach still relies on an element of appraisal and economic judgment in determining neighborhood boundaries for location effects, but it can be tested and refined by observing the effect of different neighborhood “breaklines” on the resulting three-dimensional value surface.

To be used successfully in mass appraisal, these sophisticated approaches must yield results that are reasonable, understandable and available to typical taxpayers. Lucas County has pioneered this aspect of the assessment process, as well. All real estate records, values and maps are available on a CD with GIS viewing software, priced at its production cost of $10, and online free at all public libraries in the county. Taxpayers can view property records or create customized maps showing the location of multiple parcels and the relationships among their taxable values.

Future Directions

Participants in the Lincoln Institute seminar found great promise in the Lucas County approach to location value, and identified many points for further development and investigation. All agreed that recent decades have seen a literal revolution in assessment practice, with great potential for increasing the feasibility of large-scale land valuation. Among the most important theoretical questions were the “functional form” of this spatial analysis, including the type of effect on value observed with changes in location and distance variables; the identification of omitted variables (those for which data is not available or which have been overlooked in the past); and the relationship between marginal value estimates and the total parcel value needed for assessment. Similarly, the effect of substandard buildings and less than “highest and best use” on values requires further exploration.

Development of these new approaches must be matched by educational efforts to explain their operation to taxpayers, local officials, and the lawyers and judges who will consider their consistency with legal standards for assessment practice. Through its innovative efforts in both of these areas, Lucas County has made an important contribution to the theory and practice of land valuation.

Jerome C. German is the chief assessor for Lucas County, Ohio. Dennis Robinson is vice president of programs and operations at the Lincoln Institute. Joan Youngman is senior fellow and director of the Institute’s Program on Taxation of Land and Buildings.

References

International Association of Assessing Officers. Property Appraisal and Assessment Administration (1990).

Oliver Oldman and Mary Miles Teachout. “Valuation and Appeals Under a Separate Tax on Land.” 15 Assessor’s Journal 43-57 (March 1980).

Richard D. Ward, James R. Weaver, and Jerome C. German. “Improving CAMA Models Using Geographic Information Systems/Response Surface Analysis Location Factors.” 6 Assessment Journal 30-38 (January/February 1999).

Lucas County website: www.co.lucas.oh.us