Mixed-Use Real Estate: An Options Pricing Model to Explain Behavioral Responses to Incentive Programs James R. DeLisle, Terry V. Grissom University of Washington
Abstract
In many jurisdictions, mixed-use real estate has emerged as a preferred form of development. This interest has been driven by a number of factors including the belief that mixed-use projects can create an urban form that is characterized by more vibrant, livable, walkable, urban cores, as well as networks of neighborhoods, villages and commercial nodes that are relatively selfcontained and create more “sustainable” communities. These beliefs have manifested themselves in a number of regulatory interventions ranging from zoning codes that mandate mixed-use development in targeted areas to incentive programs that are designed to encourage such development. To varying degrees, these interventions can change the type, intensity and location of development activity from what would normally occur. Indeed, in a number of cases these interventions lead to the development of projects for which portions are of questionable economically viability and thus would not be developed as free-standing uses. The objective of this paper is to explore the behavioral responses of developers and other market participants to mixed-use incentive programs when such responses may not be supported by traditional measures of demand. Of particular interest are the behavioral responses that result in is the production of mixed-use projects in submarkets and locations for which demand is limited or questionable in terms of normal locational decision-making. The empirical portion of the analysis focuses on 3-5 story residential/retail projects with special attention on the tenants who occupy the retail component of such projects. The City of Seattle is used as a case study although the results are likely consistent with those in comparable markets in which similar incentive-type programs have been implemented. The fact that there have been a series of modifications to regulatory and incentive programs affecting mixed-use in Seattle makes it an attractive laboratory environment in which behavioral responses can be quantified over time. This longitudinal analysis can provide additional insights into how quickly and efficiently developers respond to changes in such interventions. The results of this study should help developers, lenders, investors and regulators quantify the impact of various incentive programs in terms of the underlying economics, marketability and feasibility of individual projects. The study will also provide a frame of reference against which the success of such interventions can be measured. Overview
Over the past decade, mixed-use development has emerged as a preferred form of development in many urban areas. While mixed-use properties are not new, and indeed in many older urban markets has been the dominant form of development. Based in part on this success and the desire to create denser, more sustainable development patterns, local government regulations have resulted in a wave of mixed-use development has spread across urbanized areas, suburban nodes 1|Page
and smaller towns. The interest in mixed-use development has been widespread, with supporters coming from a number of perspectives. For example, supporters of the new urbanism movement have embraced mixed-use development as a means of creating more environmentally friendly, dense, balanced urban markets. Similarly, many planners have embraced mixed-use development as a means of creating more walkable, safe and vibrant neighborhoods. Developers and investors have also been drawn to mixed-use projects in an attempt to capture the positive benefits that such projects can produce for the private sector. Despite this interest and widespread adoption of mixed-use as the “preferred commercial investment opportunity,” little empirical research has been conducted into the efficacy of mixed-use development. In a recent paper, DeLisle & Grissom reported on the varied performance for mixed-use projects using Seattle as a test market. This paper is a follow-up to that earlier work with an emphasis on explaining the behavioral response of developers in spite of previous knowledge that many such projects do not deliver on promised expectations. Research Design This research involves a multi-stage approach. The first phase provides an overview of the regulatory/incentive environment in Seattle which constituted the test market. Seattle is particularly suited for exploration of mixed-use investments due to the long-standing nature of such development and the evolution of zoning, building codes over time which affected the nature and location of such development. That is, due to a series of changes at some 7-10 year intervals that occurred in Seattle, it is possible to trace the market’s reaction to “interventions.” This inquiry also provides a brief explanation of the differences in performance of various mixed-use properties. As will be discussed, the evidence that many mixed-use projects exhibit mixed-results in terms of success leads to the fundamental question of why developers seem to ignore traditional feasibility and/or marketability principles when they create new projects in response to different incentive programs. Once the behavioral responses of developers and other market participants has been explored, the research extends option pricing theory to mixed-use projects to help explain why developers may respond to incentive programs. This inquiry approaches the question from two perspectives. The first is an applied approach which explores the underlying rationale that the market adopts when developing mixed-use projects that are “incentivized” or otherwise encouraged. The second is the presentation of a more formal, theoretically based approach that extends the Fama-French three factor options model to explain the decision to deliver mixed-use projects. The Seattle Experience To develop an understanding of the Seattle Mixed-use market, the evolution of mixed-use regulations was traced over time. These interventions were then compared against empirical data on such projects were compiled from public sources. Over the past several decades, a number of initiatives have been introduces: • •
1986: Zoning to Encourage Mixed-use Development o Creating vibrant, pedestrian-oriented districts o Add housing, preserve neighborhood commercial 1988: Revisions o Tightened up commercial uses o Residential single use: higher standards & conditional use permit 2|Page
• •
1990: Revisions II o Density limits in low-rise multifamily zones o Pushed developers to commercial 1996 o Complaints after 1,400 units and 255,000sf commercial o Many vacant, financially unviable, detract from neighborhood
As a result of the changing policies, the locational distribution of mixed-use development changed dramatically as noted in Exhibit 1. Exhibit 1: Distribution of Mixed-Use Condos and Apartments Overall
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Exhibit 2 (a): Locational Distribution pre-1986
Before 1986 Morgan Junction West Seattle Junction Chinatown-International Pioneer Square First Hill Commercial Core 12th Avenue 23rd Union-Jackson Pike/Pine Belltown Capitol Hill Uptown SouthLakeUnion QueenAnne Eastlake Ravenna University District Ballard Greenlake Greenwood Phinney Ridge CrownHill Northgate 0
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Exhibit 2 (b): Locational Distribution post-1986 Incentives Built after 1986 Morgan Junction West Seattle Junction Chinatown-International District
Commercial Core 12th Avenue 23rd Union-Jackson Pike/Pine Belltown Capitol Hill Uptown South Lake Union QueenAnne EastLake Ravenna University District Northwest Ballard Green Lake Greenwood Phinney Ridge CrownHill Northgate
0
2
4
6
8
10
12
14
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20
To demonstrate how the changes skewed development to marginal areas, it is useful to look at the case study of Eastlake. As noted in Exhibit 3, after the incentive programs were put in place, developments began to spring up in areas which had limited market support from local tenants due to landform and infrastructure limitations.
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Exhibit 3: Eastlake Axial Market Mixed-Use Development
In addition to changing location and density bonuses, mixed-use incentives can be bolstered with a range of other incentive programs. Exhibit 4 provides a summary of various incentives that differ by submarket and village. These incentives create a confusing array of options that make optimal decision making problematic for developers, especially those who are not familiar with the array of options. Exhibit 4: Mixed-Use Density Bonuses
As noted, government incentives to encourage mixed-use projects included density bonuses that skewed market behavior. The end result was the delivery of mixed-use projects that likely would 5|Page
not have occurred without such interventions. Whether the end result of the incentive programs achieved the anticipated outcomes and advanced public good as expected, depends on how the market reacted and how well the ultimate projects that were delivered are received by the consumers of space. Furthermore, if the incentives created options that translate to positive market premiums, then an ancillary question is who benefits from the option. A secondary question is whether the benefit outweighs the costs. To address this question, it is useful to explore the economic viability, marketability and ultimately performance of incentivized mixeduse projects. Performance of Seattle Mixed-Use Development
The initial phase of this research focused on the Seattle Washington market. This market area was chosen in part on the basis of its long-established track record with respect to mixed-use projects. Even though all the properties are located within the limits of an individual city, they blanket the entire market. Furthermore, they are sufficient in number to allow for empirical analysis of a number of key factors that are germane to an analysis of the efficacy of mixed-use development in general, and more importantly, where such projects work and where they do not work. Briefly, the City of Seattle contains some 21 distinct urban villages which are designated as defined areas that are bound together to create distinct neighborhoods. In effect, these neighborhoods are communities within the community and have a strong sense of identity as well as a distinctive commercial core or centroid. As noted earlier, mixed-use projects are scattered throughout the market with the concentration in the central core as well as an extension along major arterials. Many of the projects are located within designated Urban Villages, and a number within areas that are delineated as the “commercial core” of those villages. However, a number of projects are also located outside of Urban Villages, either in scattered locations or along axial paths. A stratified sample of the residential/retail mixed-use projects was selected for the in-depth analysis of mixed-use projects. Once the 77 mixed-use projects were identified, attention shifted to analysis of the tenants who occupied the commercial spaces that anchored the residential/retail projects. Interestingly, only 17% of the 303 tenants in the 77 buildings were classified as retail. This tenant profiled dramatically differed from one of the key underlying premises supporting mixeduse development; the creation of vibrant, walkable, self-contained neighborhoods/villages in which residents can satisfy their needs for commercial goods. The fact that 61% of the occupants were either in the personal or professional services was somewhat surprising to the authors, but was consistent with observations of what was happening in the local market. The dominance of non-retail and non-restaurant tenants in mixed-use buildings has significant implications for the design of such buildings as well as how they function in the market and/or contribute to the local market area or neighborhood in which they are located. Another objective of the data collection was to support an analysis of the tenant “turnover” or churn rates which would provide some insights into weather the spaces “worked” for the initial tenants which could provide insights into the economic viability of such spaces for tenants as well as for developers and investors who
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depend on rental income to cover operating expenses and provide an adequate return on investment to compensate for risk. Exhibit 5: Tenant Turnover Rates
In addition to mixed performance at an overall level, performance varied widely by neighborhood (see: Exhibit 7). While many of these differences were surprising to the market they can be explained by extending urban land economics theory and market analysis fundamentals to the proposition.
Exhibit 6: Turnover Rates by Neighborhood Neighborhoods Admiral Aurora-Licton/Greenwood/Phinney Ballard Broadview-Bitter Lake-Haller Lake Capitol Hill Central Area Downtown Eastlake Fremont/Wallingford Greenwood/Phinney Morgan Junction North District/Lake City Outside of Neighborhoods Queen Anne University Wallingford West Seattle Junction Grand Total
Share of Tenants 2004 3% 3% 4% 3% 2% 2% 42% 2% 2% 1% 1% 6% 6% 8% 8% 4% 2% 100%
2004-2006 Turnover 38% 64% 38% 61% 19% 75% 49% 28% 69% 50% 25% 33% 35% 48% 58% 63% 63% 47% 7|Page
Exploration of the Behavioral Rationale Supporting MU Development Given the relatively poor performance of retail components of residential/retail projects in Seattle measured in terms of tenant turnover and developer complaints that have led to a series of modifications of the zoning code, the question turns to the underlying rationale behind developers’ production of MU projects. Before exploring this question directly, it is useful to discuss the various scenarios under which government-sponsored mixed-use programs may affect the market. There are three scenarios with respect to mixed-use (MU) development: mandated MU, excluded MU, and incentivized MU. Under each scenario land use controls have a material impact on the density and nature of development. •
•
•
Mandated MU. In the mandated cases developers were forced to include MU components to get approval to develop projects regardless of whether the retail component was feasible and supported by the market. In cases where the retail component did not pencil out, there were several “unintended consequences” or options: the residential portions had to subsidize the retail, the residual land value was lower, or the projects did not get developed. The magnitude of these impacts was exacerbated by cost premium due to differences in structural integrity, floor heights, circulation, venting and HVAC, parking, loading/service. Excluded MU. In excluded cases developers were unable to include commercial components in residential projects. In many cases this restriction was neighborhooddriven, with the underlying objective of protecting the integrity and residential character of the areas. This restriction had several impacts including: preventing the development of neighborhood-supported commercial space; creating a monopoly for existing commercial, especially non-conforming projects; reducing the walkability and economic viability of affected areas; or reducing residual land values by precluding economically viable development. Incentivized MU. In the incentivized cases developers were “encouraged” to include retail components in proposed projects which, in the absence of such incentives may not have been built or may not have been as fully developed. The end result was an increase in density measured in terms of sheer volume of developed space that occurred per unit of land. In such cases, if there was sufficient demand for the retail component the profit margin for the developer might increase which could be treated as an option. The value of that option would be the net present value of the increase in income compared to the cost to create and operate the incremental space.
Clearly, the search for higher profit margins for developers is an intuitively and theoretically valid reason for the development of mixed-use projects. While this outcome will be explained in the application of the Fama-French Three Factor model, it should be noted that there are several possible outcomes from incentivized MU programs that could render such strategies invalid. This caveat is especially true where the incentive programs are overlaid on market areas within 8|Page
which demand may not be sufficient to support retail, and/or in cases where transportation systems, corridors and/or parking are not adequate to attract other customers. In such cases, if the retail component does not generate sufficient income on its own to be “unit profitable,” then the residential component must subsidize the retail. To explain this situation, it is useful to look at the basic business model of a retailer who is targeted for occupancy of a mixed-use building. As noted in Exhibit 7, in selecting locations for stores, retailers typically apply a Real Estate Capture Ratio (RCR) to gross sales to calculate what gross rent the individual outlet can afford to pay. Basically, the RCR establishes the “total occupancy cost” ratio for a targeted location. While the RCR can vary by retailer based on the other cost components (e.g., labor, costs of goods, operating expenses) it is also anchored by competitors. For example, if competitors allocate 14% of total gross sales to real estate occupancy costs, then a retailer cannot charge significantly more without becoming the “high cost” provider. This is particularly true with respect to general goods and services that operate in a highly competitive and efficient market. Exhibit 7: Retailer Total Occupancy Cost Model
A simple example can illustrate the basic model. Assume the analyst for a retailer is looking at locating a facility in a new mixed-use project. As in the case of dedicated retail facilities, the analyst would begin with the delineation of the primary and secondary trade areas for the outlet. Once the trade areas have been delineated, the retail sales potential for the types of merchandise that the retailer offers would be quantified. At this point, attention would turn to competitive analysis to determine the “capture ratio” or percent of total sales that could be expected for a facility located at the targeted site. Once the gross sales potential has been quantified, the RCR (or Total Occupancy Cost) ratio would be applied to estimate the gross rent that could be paid. To simplify the analysis, it can be conducted on a per square foot basis and then extended to the projected size of the facility. For example,
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Gross Sales/SF RCR Gross Rent/SF
= = =
$220 14% $ 30.80
Now, assume a developer would require an 8% cap rate to justify the project. If the Net Income Ratio (NIR) was 72%, the Net Income in the market to the developer would be: NIm/SF R
= =
$ 22.18 8%
TRCj/SF
=
$277.20
If the Gross Sales/SF fell to $180, and the RCR was 12%, the TRCj/SF would fall to $194.40 which could be lower than the Total Replacement Cost (TRCm) in the market to create the space including hard and soft costs as well as land. Now, if the developer can reduce the marginal land cost/SF by using an incentive program, then the retail portion of the project might pencil out. Alternatively, when considering the density bonus (i.e., the addition of a floor of residential), the land cost avoided or not needed to satisfy land use controls could be used as an implicit subsidy to the retail required rents. This would make sense to a developer since added the retail component triggered the higher density and hence lower land costs. In cases where the retail component does not generate sufficient income on its own to be “unit profitable,” then the residential component must subsidize the retail. Some of this subsidy is built into an incentivized mixed-use project in the respect that the developer can avoid the cost of acquiring additional land to support more residential units. For example, in Seattle in certain designated zones if a developer voluntarily adds retail to a residential project, the density can be increased through the addition of another floor of residential and a waiver of a height restriction for the additional floor. Thus, the developer can spread the original cost of the land over the original, entitled residential area plus the incentivized residential area and the added retail area. The benefit comes from the fact that the latter two components are essentially built without any incremental land cost which provides a competitive advantage over neighboring uses that are single use, either residential-only or retail-only. Consider the following for a one acre site located in a targeted MU-incentive zone. The basic equation for the maximum building envelop is: (GSSF * LC) / [(1 / BN) + (1 / (1000 / PI)) * (PS / PN)]
Where: GSSF = Gross Site Size. The total gross coverable square footage of site net of open space requirements, LC = Lot Coverage Ratios. The maximum percent of a site that can be covered by buildings and/or parking structures,
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BN
PI PS PN
= Building Number of Floors under Height Restrictions. A maximum on the linear footage or number of stories above ground that a building can be developed, = Parking Index. The index of parking requirements that states the number of stalls per 1000 square feet of building, = Parking Size. The average size of each parking space including circulation. = Parking Number. The number of Stories for decked/ramp parking.
Consider the following scenarios for a site: •
•
Residential Only. If the GSSF is 43,560, the LC is 80%, the BN is 3, the PI is 1.2 (per unit, 800 SF average unit size), and PS is 350SF/stall, the maximum residential-only building is 46,258SF for a 3 story building. In this case, the lot coverage or footprint of the building is 15,419 SF. Mixed-Use. In this case, the developer agrees to add retail to the first floor of the property in return for an additional floor of residential units. Using the same assumptions, the maximum building size is now 61,678SF which includes 4 stories of residential (i.e., 15,419 * 4) plus one story of retail at the same footprint.
Under the mixed-use scenario, the developer receives a premium of 1.33 more building space without buying additional land. Without the bonus, at $120/SF of land which is typical in Seattle, the developer would have to pay an additional $6,969,600 for land (i.e., 43,560 * 33% * $120). At an 8% cap rate (R), the implicit annual subsidy that could lower the retail rent and/or residential/retail combined net income would be $557,568 which could make the project pencil out.
In the previous discussion the option value may prove to be elusive with any excess eaten up by the implicit subsidy and added costs associated with higher density and per unit costs, greater parking requirements that could trigger underground parking, and offsets required for underperforming components of the projects. In other cases, the option value may be real but the increased value may not be captured by the developer but by the land owner. In effect, if the incentivized MU projects penciled out in an areas, the excess returns which came from reduced land costs would be quickly erased due to the efficiency in the land market. That is, once a more intense or higher and better use was demonstrated for a certain area, landowners would quickly increase land prices to the new supported residual value. This could be achieved by capitalizing the excess income the project would generate over the incremental cost, or by using a land ratio multiplier (e.g., land – 20% of total cost). If this occurred, then the “incentivized MU” treatment would effectively be rendered a “Mandated MU” treatment due to the fact that more intense utilization would be necessary to justify the higher land costs. Some might argue that the question of who captures the benefits of the option value is immaterial, that the end result of more MU projects was the ultimate goal. In effect, to such observers and/or advocates of MU the implicit conversion of an incentive MU program to a mandated MU program achieved the desired outcomes. From a market-based perspective, the 11 | P a g e
cost of the outcome may be much higher than realized in the sense that the incentive program could trigger the development of functionally obsolescent space; space that is not financially feasible. Option/Portfolio model of MU Development As noted above, mixed-use development and development in general has been approached economically and behaviorally from two general perspectives. The first focuses on the profitability and financial feasibility as discussed above. The option approach is linked traditionally into choosing the timing and use that maximizes land value. This conceptually links into an extension of the feasibility analysis that if appropriately structured can be organized to consider multiple and alternative mixes of uses within the option framework. The focus on mixed-use moves the modeling from a concern of maximizing land value conditioned on a use or project proposal to one of organizing the site’s use mix to optimizing the project return/performance subject to the risk exposures encountered; see Grissom, Berry and Lim (2010). This can be achieved by structuring the expected return to be produced from any development scenario as a function or condition of project risk as determined by the project organization and land use or land use mixes. This latter component is addressed by comparing the differencing between single and multiple use projects return potentials relative to risk levels. This comparative construct can be established by viewing a single land use development as a single asset or option, while the mixed-use project is effectively a portfolio of land uses and projects that is subjected to diverse risks and covariance affects. This can be achieved with a modification of the Fama-French 3 factor asset pricing model. This model besides considering the markets impact on asset or project performance also addresses the impact of project size (its log or change) and the ratio of the asset’s profitability stated as the ratio of its capitalization (or market weighted value/current price) to its book value (previous price/cost or purchase price). This model is discussed in detail by Fama and French (FF) (1992) and Grissom et al (2010) as the procedure is applied to real estate development. The ratio of market value to cost as constructed in the FF technique, compares directly to the profitability ratio or probability of success (or ruin) that can be developed from a ratio comparing the back-door approach to the front-door approach addressed in the analysis presented earlier. In turn this integration of real estate financial feasibility to option theory to the Fama-French construct is premised on the linkage of distributive theory (the accounting of the compensation required by all factors of production with the theory of valuation as it focuses on the concern of alternatives/options in measuring the value of proposed projects (mixed or single uses). To link the previous sections that develops the specification of alternative development scenarios/options with the financial feasibility necessary to investigate the economic/market potential of a project, it is necessary to identify the influences of the foundations of distributive and valuation techniques as they are incorporated into a FF valuation model.
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Extension of Options Pricing Model to Explain Developer Behavior Given the anecdotal evidence that mixed-use development may not achieve the desired goals and objectives, a logical question to ask is “why do developers produce mixed-use projects, especially in areas limited market demand for the retail component? In applying the model, several factors were considered including: •
Development decisions/strategies are highly associated if not dependent on development valuation analysis
•
This valuation approach is directly linked to the constructs of distributive theory requiring a consideration of value differences and potential profits that arise from differing land uses associated with alternative project proposals
•
Distributive theory requires an analysis of the return or compensation for each of the factors of production that define economic production, growth and development
•
This requires that consideration of the differences in returns to the varying levels of distributive factors of labour, capital, land and entrepreneurship and their associated risk exposures that may arise under proposed alternative developments possible on any given site.
•
This approach combines the options approach to development with the traditional production/manufacturing process
This integrated approach to development: option and production fits one scenario of capital theory defining capital as the “product of production” and as investment (economic measures of time) See Ahmed (1990). Like the front-door/back-door model and procedure discussed above, the modified FF three factor model allows labor and capital (real and financial) to be integrated in the process as development inputs. This then allows, as with the front-door/backdoor procedure for the distributive measures of land and profit to be alternatively calculated as residuals. These residual returns are conditioned as the dependent (determined) measures. These expected (ex ante) returns/along with the total returns measure of performance defined as functions of by risk and uncertainty variables directly fits into the context of the Fama-French Three factor model (1992). The integration of property development procedure with a risk-return financial construct requires a few more modifications. Instead of relying on market risk as a source of exogenous shock, the spread in financial payments and leverage effects are considered, since real estate is exposed to external financing (not the internal financing experienced and employed in many corporate/business analytics). As such the sensitivity of development exposures to financial impacts and shocks on the spread in debt payments may be more significant to expected returns that new developments that are often intended to vary from existing market norms.
The leverage adjustment is combined with the size and profitability measures previously discussed and presented as the variables to be applied in Exhibit 8. Exhibit 9 and Exhibit 10. The underlying pricing model based upon the variables developed in Exhibit 8 are presented for first a single use property and then a mixed-use development. The proposed model is then applied to an out- of- sample database using Belfast project data and experiences to test the model. 13 | P a g e
Exhibit 8: Application of Three Factor Model
Exhibit 9: Three Factor Model for Single and Mixed-use Development
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Exhibit 10: Extending to Multiple Mixed Use
Rationale for Extending Fama-French to Mixed-Use Equating mixed use development to a portfolio of uses subject to risk associated with alternative combination of uses is an extension of the option approach adopted by real estate, financial and urban economists. See Grenadier (1995,1996), Williams (1991, 1993), Capozza and Henley (1989) and Geltner and Miller (2001). Consideration of development as a strategic choice of alternatives also allows the use of data development from development valuation reports employed by practitioners, lenders and others in conjunction with existing projects to directly consider the impact of proposed development to developments under construction and completed. This enables a direct specification of entrepreneurial profit related risk and not just the risk associated with capital risk that influence the pricing of operating projects. This data in turn is a function of the random market and project specific data available in any given market. Use of the development valuation reports for given sites and proposed projects offers an empirical approach that is similar to the simulation derived from developing an array of alternative portfolios based on different weightings of securities and general economic data. The following statistical analysis using the Fama -French model follows the procedures set in the urban land option pricing procedure. In this regard the focus is on the analysis of the land residual values and its optimization. This will differ from techniques observed in the British literature, which will also consider profit residuals. Exhibit 11 focuses on estimates of the total return to mixed-use projects derived from the sample observations. Based upon the land option perspective Exhibit 12 through 13 reflects land residual measures.
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Exhibit 11: Statistics and Coefficients Yield (Total Return) per Project for Proposed Use Mix
Exhibit 12: Statistics and Coefficients for Residual Land Value Difference by Project Proposed Use Mix
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Exhibit 13: Statistics and Variable Coefficients for Residual Land Value Difference by Mixed Project and Additional Land Uses
Conclusion Yield analysis from the proposed model is consistent with the findings of systematic investment portfolios using the Fama-French model (See Bond, Karoyli, Sanders 2004). This research is broader as applied to development in three perspectives: • • • • • •
in allowing the consideration of mixed uses not just the difference in alternative development scenarios as offered by option models in allowing for the consideration of combination of uses (a mixed portfolio) as the use mix impact land values measures and profit as a residual calculation in allowing for the incorporation of risk and uncertainty into traditional valuation and land economic measures of performance The performance of office and retail synergies within a mixed-use development increases all measures (yield, profit and residual land value) compared to the dominance of a single use. Residential within a mixed-use development increases the complexity of a risk and market model as a consequence of mixed tenure housing/affordability housing provision The inclusion of hotels in a mixed-use development contribute to a residual land value enhancement over a single use development.
In summary, mixed-use development increases land residual value relative to risk levels and may offer strategy to hedge recessionary impact. The findings of the model emphasises the benefits of entrepreneurial developer’s effort of innovation and strategic management in value creation. It also helps explain some of the complexity in multiple incentive programs that should be explored in a behavioural context. Finally, the incentive mixed-use case study applied to the one-acre development scenario in Seattle demonstrates that incentive programs may achieve the public policy goal of increasing such product. However, this outcome may be at the expense of better uses of scarce resources, especially if the land market is efficient and any benefits from the incentive option is captured by the landowner. In such cases, projects may be rendered infeasible until land prices come down and/or other demand/supply factors change to offset higher costs of production.
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Selected Bibliography
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