Housing-market bubble adjustment in coastal communities - A spatial and temporal analysis of housing prices in Midwest Pinellas County, Florida

Applied Geography 80 (2017) 48e63

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Housing-market bubble adjustment in coastal communities - A spatial and temporal analysis of housing prices in Midwest Pinellas County, Florida Yin-Hsuen Chen*, Timothy Fik Geography Department, University of Florida, Gainesville, FL, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 June 2016 Received in revised form 24 January 2017 Accepted 24 January 2017

This study aims to investigate the spatial and temporal dynamics of housing prices associated with the 2007 U.S. housing-market bubble in Midwest Pinellas County, Florida. Two methods were used to examine the spatial and temporal dynamic of price levels: housing characteristic influence estimation and a hedonic modeling approach. Two consistent spatial patterns emerged in the estimated coefficients associated with various housing characteristics, with definitive changes occurring at 600 m and 2200 m from the coast. These changes suggested the existence of three geographical submarkets: a coastal, an intermediate or transitional, and an inland submarket. Three temporal stages were observed from the coefficient trends associated with various housing characteristics: 2002e2005 (bubble-growing), 2006 e2008 (bubble-burst), and 2009e2011 (post-bubble). The hedonic models demonstrated a complexity in the determinant of housing prices during the housing peak-bubble period. The inclusion of submarket dummy variables in the hedonic models improved the amount of explained variation. The R2 values of the hedonic models from 2002 through 2008, followed by a decrease after the bubble-burst period. The pre-bubble-burst trend in R2 suggests that predictable market forces were at work; partly driven by irrational expectations of housing buyers and the perceptions of run-away housing prices during the growth phase of the bubble. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Housing-market bubble Price adjustment Coastal amenity Hedonic model GIS

1. Introduction The recent nation-wide housing-market bubble had a marked effect on housing values, causing an escalation and fluctuation of price levels from the late 1990's to the 2007. From 2007 to 2008, however, housing prices fell by as much as 30 percent in the U.S (Baker, 2008). with losses in equity leaving a large number of new owners “upside down” in their mortgages. The market value of homes continued to slide from 2008 to 2012, reaching new lows in nine major Metropolitan Statistical Areas (MSAs) by February 2012 (McGraw - Hill Financial, 2012a). Numerous studies began to focus on the causal nature of the housing-market bubble, and the supply and demand conditions which led to the market run-up of housing (Levitin & Wachter, 2012). For example, Case and Shiller’s (2003) demand side analysis clearly highlighted the role of speculative

* Corresponding author. 3141 Turlington Hall, P.O. Box 117315, Gainesville, FL 32611, USA. E-mail address: eisen520@ufl.edu (Y.-H. Chen). http://dx.doi.org/10.1016/j.apgeog.2017.01.007 0143-6228/© 2017 Elsevier Ltd. All rights reserved.

investment and consumer perceptions regarding inflationary pressures in real estate markets. Although the collapse of the housing-market bubble brought a severe recession to the entire U.S. financial system, the housingmarket bubble occurred at different levels of intensity across the nation. In short, there was variability in the degree to which price levels rose across various regional markets and sub-markets (Goodman & Thibodeau, 2008; Martin, 2010; Mayer, 2011). Krugman (2005) concluded that the greatest impact of the housing-market bubble happened in what he labeled the “Zoned Zone”, geographic areas located along the coasts with high population density residing in limited spaces. He also concluded that the Zoned Zone was more prone to this type of run-up due given that these properties in high amenity coastal areas, where consumers would be fearful of missing out on buying a property. Several recent studies had focused on the elasticity of housing supply across U.S. (Davidoff, 2013; Saiz, 2010). Saiz (2010) applied the terrain model to examine the relationship between housing supply elasticity and the land-constrain geography in the metropolitan areas during the 2010s housing bubble. He concluded that

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people were willing to pay higher housing prices in coastal area due to the valuable amenities and the scarcity of construction land; as such, local housing markets near coastal areas experienced a greater bubble-led price run-up when compared to interior areas. Large differences could be found in the housing price indices associated with coastal versus inland MSAs during the years associated with the bubble's formation (Goodman & Thibodeau, 2008). While a good deal is known about the drivers of the housing market bubble, relatively fewer studies have focused on housing price adjustment at the local scale. A comprehensive investigation of local housing price structures is necessary to understand where and when the housing market bubble affected in coastal communities, and to what extent it affected price levels as one moved inward from the coast. This study aims to shed light on the adjustment of housing prices during the U.S. housing market bubble by analyzing the market prices of single family housing units in and along a coastal community in the years leading up to bubble and the years following its demise. The intention here is to highlight the change and differences in the spatial and temporal distribution of average market price adjustments of housing for three distinct submarkets areas located in a coastal community as defined by increasing distances from the coast. To reach this objective, housing price and characteristic data were obtained for eight zip-code areas in Midwest Pinellas County, Florida. The study relies upon the estimation of market value/price using a hedonic approach based on housing characteristics and location. 2. Hedonic models Previous studies have applied hedonic models to investigate the impact of various housing characteristics on price and determine the structure of housing prices and buyer's willingness to pay for various attributes or amenities. Numerous studies have applied a hedonic analysis to examine the influence of coastal amenities on housing prices, which is typically captured in the market price/ value of housing as it declines with increasing distance from the coast and price difference between coastal and inland properties (Benson, Hansen, Schwartz, & Smersh, 1998; Conroy & Milosch, 2011; Hamilton & Morgan, 2010; Pompe & Rinehart, 1994). For example, Conroy and Milosch (2011) studied market prices of houses in the San Diego area and concluded that the distance of a house to the coast plays a significant role in the determination of housing prices. Conroy and Milosch's study (2011) contended that there is a non-linear relationship between coastal amenities and housing prices, given the heterogeneous characteristic of housing and the structure of prices across locations. To examine the effect of heterogeneous characteristic, several studies have included components to account for housing market segments or submarkets. The basic concept is that properties with similar site and situational characteristics or within certain submarkets tend to be close substitutes in comparison to properties with dissimilar characteristics. By adding submarket affiliation to the hedonic model, a more accurate estimation of housing values can be made (Bourassa, Hoesli, & Peng, 2003). Submarket analysis have considered the temporal dynamics of housing submarkets (Goodman & Thibodeau, 2007; Goodman, 1981; Pace, Barry, Clapp, & Rodriquez, 1998; Tu, 1997; Watkins, 2001), as well as the structural and neighborhood characteristics of hedonic model coefficients; concluding that these characteristics were not constant across space and time. Hedonic models have also been utilized to determine the adjustment of housing prices during the formation of housing market bubbles, where the market price of housing rises relative

49

faster than its true underlying appreciation (Dorsey, Hu, Mayer, & Wang, 2010; Shimizu & Nishimura, 2007; Smith & Tesarek, 1991). Dorsey et al. (2010) applied spatial hedonic regression and repeatsales indices to identify the boom-bust cycle in Los Angeles and San Diego metropolitan areas, noting that the impact of the housing market bubble was dissimilar in these two geographic markets. Not only did their analysis show that the booming peaks of the housing-market bubble was different for these two cities, but the boom-bust cycle changed across zip code areas. This suggested that intra- and inter-MSA variability existed during the boom-bust cycle, and that metro-area trends do not necessarily capture variability in local conditions as the market cycle runs its course. Most of the previous temporal/submarket-based housing-market bubble studies utilize a single hedonic model incorporating temporal metrics or dummy variables by year. While it has been argued that this approach improves the accuracy of models, it is also acknowledged that it is difficult to differentiate temporal trends based on individual years when there are changes in the designation of submarket coverage. 3. Study area The study area is located on the Midwest Coast of Pinellas County, Florida (see Fig. 1). The market values of housing units found within eight zip codes were analyzed: 33755, 33756, 33767, 33770, 33774, 33776, 33786, and 33785. Three zip codes areas were located on the barrier islands, with the others associated within either inland areas or coastal lagoon areas. Pinellas County is an area that has experienced rapid economic growth and a shift to a retail-and-service orient economy due to coastal amenities and an influx of post-World War II retirees, factors which helped to raise prices in the local real estate market during the decades of the 50's, 60's, and 70's. According to the parcel data, about 52% of parcels in the study area are single family residences; with most of the land in the county having been developed. Pinellas County is part of Tampa-St. Petersburg-Clearwater MSA. According to previous studies (Goodman & Thibodeau, 2008; Mayer, 2011), Tampa MSA was one of the areas that experienced an extreme housing market bubble. Fig. 2 illustrates the rise in the S&P/Case-Shiller House Price Index (HPI) for the Tampa MSA from 100 to 234 from 2000 to 2006, and its marked decline to 127 from 2006 to 2011. The gray area indicates the chosen study period: 2002 to 2011. Fig. 3 highlights the numbers of single family sales from 2002 to 2011. On average, there were about two thousand housing sales per year from 2002 to 2005. After 2007, the number of sales dropped to about one thousand. The HPI shows that housing prices peaked in 2006, during a year when sale numbers were showing a tendency to decline. This is a typical time lag in a boom-bust cycle, were prices continue to rise and then peak as the popularity of the housing market begins its rapid decline and signals go out to buyers that the market is retracting. 4. Methods 4.1. Data preparation The housing price data were downloaded from the Pinellas County Property Appraiser (PCPA) website (Pinellas County Property Appraiser, 2013). The data searches were based on single family sale records for each year from 2002 to 2011, and market values were selected to represent housing prices. In order to remove the effect of housing price inflation, the HPI of the Tampa MSA (McGraw - Hill Financial, 2012b) was applied to correct housing prices. Other than housing prices, shapefile layers - which include zip codes, parcels, road networks, shoreline, and flood

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Fig. 1. Map of the study area. The black dash lines illustrate the boundary of eight selected zip codes.

zones - were downloaded from Pinellas County GIS Data Sets websites (Pinellas County Government, 2012), and Flood Map Service Center of Federal Emergency Management Agency (FEMA, 2013).

4.2. Housing characteristic influence estimation (HCIE) In order to understand the change of housing characteristic influences over time, three housing structural characteristics and two coastal amenity characteristics were selected to build multiple regression models, which are written as:

yi ¼ b0 þ

3 X j¼1

bj Sij þ

2 X

bk Cik þ εi ; i ¼ 1; …; Js

(1)

k¼1

where yi denotes the market value of housing (i.e., the observed selling price of housing unit for a given year), Sij represents three basic housing characteristics: gro_acre (lot size in acreage), living_space (square footage of living space), and SF_density (density of single-family houses). Cik represents two coastal amenity characteristics: acc_beach (distance from house to public beach access) and dis_water (distance from house to water). The b0 denotes the

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Fig. 2. The S&P/Case-Shiller Home Price Index of Tampa metropolitan statistical areas from 1987 to 2011, the gray area presents the selected study period.

Fig. 3. The sales numbers and Home Price Index from 2002 to 2011.

intercept of estimated prices, and bj and bk indicate coefficients of variables, whereas εi captures the random error (i.e., the residuals), for i ¼ 1, …,Js sample observations in a given year for a given distance to the shoreline. For each year of the boom-bust cycle, 90 multiple regression models were estimated based on increasing Euclidean distance from the shoreline, for the set of single-family housing units contained within that distance. The distances ranged from 100 m to 4500 m with a set 50 m interval between each model. For instance, a 600-m model contained single family housing units that were within 600 m of the shoreline. As the distance from shoreline increased, more housing units were included in model (see Fig. 4). The cumulative sum control chart (CUSUM) method was utilized to verify the critical changes of estimated coefficients from the regression models as the distance from shoreline increased. The CUSUMs were plotted as charts by calculating:

Ci ¼

i   X xj  m0

(2)

j¼1

where xj is the average of jth coefficient value, m0 is the mean of the coefficient values. The CUSUM charts were applied to detect the change of coefficient values over the expansion of HCIE, with t-tests used to test if the critical distances at which changes in coefficient values were consistent during the study period for the variables included in the HCIE model.

4.3. Hedonic analysis A hedonic model was used to identify the change of relationships between housing prices and housing characteristics during the housing-market bubble era. The hedonic model can be written as:

yi ¼ b0 þ b1 xi1 þ b2 xi2 þ … þ bp xip þ εi ; i ¼ 1; …; n

(3)

where yi is the market value of property, b0 is the intercept of estimated prices, b1 to bp are coefficients of housing characteristics (xi1 to xip), and εi denotes the random error, which represents unobserved variations unaccounted for by the model. A region-based hedonic model was first constructed to identify the variables that accounted for a large and significant share of the variation in prices during a chosen base year. That model was then used as a template models for each of the other years; using the same set of independent variables, to allow comparisons to be made across years in the estimation of coefficients associated with specific housing attributes. The year 2005 was selected as the base year because it marked the end of higher sale numbers during the boom-bust cycle within this study region (Fig. 3). Table 1 lists the variables included in the 2005 model. Housing characteristics were downloaded with sale records from the PCPA, and other variables such as accessibility variables and neighborhood variables were computed using ArcGIS. The distances from single family houses to the public beach access, as

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Fig. 4. The expanded coverages of models for housing characteristic influences estimations. 2005 sale records are plotted on this figure.

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Table 1 The descriptions, abbreviations, types and sources of variables. Variable (description)

Abbreviation

Type of Housing Characteristicc

Sources

market value (dependent variable) Gross area On barrier island or not Number of stories Effect building year Living area Value per unit area Quality of property Sea wall dock Distance from house to public beach accessa Distance from house to major access of barrier island Distance from house to the exits of the Pinellas County on East Distance from house to St. Petersburg downtown Euclidean Distance from house to nearest shoreline Single family density Flood Zone e Ab Flood Zone e Vb

mv gro_acre barrier stories build_yr living val_unit quality seawall dock acc_beach acc_BI exit_EPP dis_STP dis_water SF_density floodA floodV

e lot characteristic lot characteristic structural characteristic structural characteristic structural characteristic structural characteristic structural characteristic structural characteristic structural characteristic accessibility variables accessibility variables accessibility variables accessibility variables accessibility variables neighborhood variables n/a n/a

PCPA PCPA Location analysis by ArcGIS PCPA PCPA PCPA PCPA PCPA PCPA Interpreting from FDOT aerial photos Network analysis Network analysis Network analysis Network analysis Euclidean distance analysis Point density analysis FEMA FEMA

a

The public beach accesses were based on the number of parking spots, only the part area has more than 50 spaces were selected. Both flood zone A and V denote a one percentage annual chance of flooding, and zone V with an additional hazard associated with storm surge. c The housing characteristics categorized by Basu and Thibodeau (1998) included lot characteristics, structural characteristics, neighborhood variables, accessibility variables, proximity externalities, and landuse variables. b

well as the determination of major access routes to barrier islands and the eastern exits of the Pinellas County and Downtown St. Petersburg, were computed based on a road networks. Table 2 lists the descriptive statistics of the dependent variable (market values). Note that logarithmic and square-root transformations were considered and applied accordingly to the dependent and independent variables of the model to enhance the fit as suggested by Baranzini, Ramirez, Schaerer, and Thalmann (2008). For the base model, all independent variables were tested for significance at the 95% confidence level. In addition, the variance inflation factor (VIF) of each independent variable was restricted to be less than 7.0 to side-step the effects of multicollinearity in the significance testing of coefficients. In addition, suspected outliers were identified from an analysis of the residuals and highly leveraged observations removed. Standardized residuals, Cook's distance, and DFFITS values were used in the leverage diagnostics. The base model for 2005 was then applied to each year to allow estimation of a set of coefficients for each independent variable such that the change of coefficient values during the housing-market bubble could be analyzed over entire boom-bust cycle. In order to understand the temporal trend of coastal premiums associated within each geographic submarket, a sample of n ¼ 110 sample houses were selected from the study region and the hedonic model in equation (3) estimated for each year of the boombust cycle. To highlight the influence of coastal premiums, structural characteristics were held constant, with the mean values of the independent variables for the entire data set used to estimate

market prices across submarkets. Among the 20 houses residing on the barrier island, ten houses were beach-front properties within Flood Zone V and ten were located away from the water. The remaining 90 inland houses in the sample were located on the mainland of the Pinellas Peninsula. The sample selections were based on the distances between the single family houses and the nearest coastal water-access boundary. Note that two housing units were picked at random from each sequentially expanding zone moving at 100 m increments away from the coast. Fig. 5 illustrates the location of the housing units in the sample. 5. Results 5.1. Housing characteristic influence estimation (HCIE) The housing characteristics influence estimation (HCIE) revealed non-linear influences of housing characteristics on housing prices and a complexity in housing price structures across submarkets. Fig. 6A through 6I highlight the adjusted R-squares (6A), the intercepts (6B), and the estimated coefficients of the independent variables (6C to 6G) along the y-axis, with the x axes representing the expanded distance from the shoreline. As the xaxis increases, more observations were included in estimation models. During the majority of years, the R2 slightly decreased as the model expanded. This is most likely a byproduct of the model's inability to capture the effects of mixing housing units from multiple geographic submarkets. Fig. 6B illustrates the result of the

Table 2 The description statistic of market value from 2002 to 2011, all market values shows here had been corrected based on the Home Price Index (HPI). The sale numbers and HPI are also listed in this table. year

Average

Standard deviation

Minimum

Maximum

Sale numbers

HPI

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

111,524 111,722 107,662 100,407 106,892 132,773 133,852 143,324 130,425 128,486

112,250 116,117 103,854 97,701 139,385 178,681 124,245 152,290 129,095 134,978

5553 3016 6387 7031 9812 14,358 13,261 16,544 5183 8825

1,632,239 1,480,612 1,564,156 1,179,425 2,658,343 1,989,997 1,769,543 1,814,347 1,406,790 1,601,212

2117 2029 2119 2055 1400 981 869 1025 984 988

1.2606 1.3926 1.5969 2.0053 2.3442 2.1661 1.7495 1.4210 1.3638 1.2741

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Fig. 5. The location of 110 sample houses for estimating the premium of coastal premium during the housing bubble.

Fig. 6. The coefficient slopes of the housing characteristic influence estimation. The result of adjusted R-square and coefficient slopes of four basic housing characteristics and two coastal amenity variables are demonstrated: A) Adjusted R-square, B) intercept, and the coefficients of C) gross area, D) living area, E) single family density, F) distance from house to public beach access, and G)Euclidean Distance from house to nearest shoreline. Some of the values in Fig. 6AeH were excluded in order to better demonstrate the emergent patterns.

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Fig. 6. (continued).

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intercepts, which can be seen as the initial value of housing units when all other variables are zero. As this figure shows, the largest values and greatest variability in the intercepts occur at distances between 100 and 600 m to the shoreline. Beyond the 2200 m mark, the differences in intercepts become noticeably smaller as one moves toward a model that encompasses the entire regional singlefamily housing market. This result indicated that the average housing prices tend to decrease toward a converging regional average as the model expands to include housing units that were increasingly distant from the coast; that is, as the effect of the coastal amenity lessened. The post-bubble years of 2009 and 2010 were exceptions, as the intercepts increased as the model expanded from 100 to 600 m and then began to decrease around 2200 m. This result suggested a market that has stabilized within sub-markets, with prices that reflect better the market value of location. Note, here the 600 and 2200 m were visually selected for further analyses; in reality, the submarkets were separated gradually. Table 3 highlights the t-tests for the average CUSUM values of the three identified submarkets based on the patterns observed at two critical distances: 600 m and 2200 m; which were consistent over the study period. Fig. 6CeE displays the estimated coefficients of three basic housing characteristics: gro_acre (lot size in acreage), living_space (square footage of living space), and SF_density (single-family housing unit density). Variation of coefficient values was observed to be greater for distances between 100 and 600 m from shoreline than at distances beyond 600 m. As distance from shoreline increased, estimated coefficients flattened out, especially for distances beyond the 2200 m mark. An interesting temporal trend was observed for the coefficients associated with living space (see Fig. 6D). In particular, three stages were distinguished: 2002 to 2005, 2006 to 2008 and 2009 to 2011. Most noticeable was the increasing trend of the living_space coefficient during the formation of the housing-market bubble, suggesting that the price of larger houses tended to rise faster than smaller units during the boom-bust cycle. Fig. 6FeG illustrate the coefficient changes of two coastal amenities: acc_beach (distance from house to public beach access) and dis_water (Euclidean Distance from house to shoreline). The influences of these two coastal amenities generally decreased as the model expanded to cover the entire study area, thus indicating that the value of coastal amenities (as represented by distance to public beach access and the shoreline) was relatively higher in coastal areas, with a pronounced distance-decay property as would be expected. For instance, the coefficients of dis_water changed radically from roughly 1200 to 200 (Fig. 6G), with the largest variation again observed in 100e600 m range, with coefficients that stabilize and become flat after the 2200 m from shoreline. Three distinct time stages can also be observed for the estimated

57

coefficient associated with beach access (acc_beach, Fig. 6F), with coefficients showing less variability and remaining flatter during the pre-bubble years (2002e2005). The results suggested that the coastal submarket was less differentiated from the rest of regional market during the boom-bust cycle in terms of distances to public beach access. There were two critical changes observed in the estimated coefficient values obtained from the HCIE results. These two critical distances designated three distinct housing submarkets for singlefamily units: a coastal market (located from the shoreline to roughly 600 m), an intermediate market (ranging from about 600 to 2200 m), and an inland market (at distance of 2200 m or greater from the shoreline). These critical distances were added as gray shades on Fig. 6. Fig. 7 illustrates the spatial coverage of 600- and 2200-m models; where most of the barrier islands and the lagoon fronts were included in the 600 m coastal submarket model. While the critical distances at which coefficient changes occurred did not have substantial shifts over the study period, changes in values of coefficients with increasing distances from the shoreline within each year of the boom-bust cycle were very pronounced. This result suggests that although the geographic coverage or delineation of the three submarkets did not change, the value (and hence influence) of various housing characteristics changed from year to year depending on where the market was in terms of the boom-bust cycle. Moreover, three time stages were noticeable, when analyzing the results of the estimated intercepts and the coefficients associated living_space, and acc_beach variables. The results showed a bubble-formation stage: 2002 to 2005, a bubble peak: 2006 to 2008, and the post-bubble period: 2009e2011. 5.2. Hedonic analysis The hedonic model results demonstrated a complicated housing price structures over the study period, and the importance of coastal submarket. The increasing adjusted R-square revealed the irrational expectation of housing buyers during the bubbleformation stage. Table 4 summarizes the results of the 2005 baseyear hedonic model. Eleven explanatory variables were found to be statistically significant at the 95% confidence level, with an adjusted R2 of 0.85. Among the eleven variables identified, seven variables were related to coastal attributes (barrier, seawall, dock, acc_beach, acc_BI, dis_water, floodV), demonstrating the importance of coastal amenities to the housing prices. The significant variables from the base model were then applied to the other years within the boom-bust cycle. Table 5 lists the mean and standard deviation of the variables used in the hedonic analysis; and Table 6 shows the coefficients and corresponding t-values for each of the variables. Note that for each year, two modeling frameworks were

Table 3 The probability of Student's t-test result for two critical distance based on the CUSUM values of HCIE. Variables intercept gro_acre living SF_density acc_beach dis_water

600 m 2,200 m 600 m 2,200 m 600 m 2,200 m 600 m 2,200 m 600 m 2,200 m 600 m 2,200 m

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

0.000 0.264 0.183 0.000 0.505 0.000 0.002 0.000 0.000 0.000 0.224 0.000

0.000 0.002 0.004 0.000 0.001 0.000 0.015 0.000 0.001 0.000 0.177 0.000

0.000 0.018 0.000 0.001 0.857 0.000 0.000 0.796 0.000 0.582 0.056 0.000

0.000 0.004 0.000 0.013 0.498 0.000 0.000 0.271 0.007 0.000 0.058 0.000

0.000 0.082 0.650 0.000 0.000 0.236 0.000 0.000 0.197 0.000 0.021 0.000

0.002 0.000 0.003 0.000 0.647 0.000 0.651 0.000 0.000 0.000 0.125 0.000

0.000 0.019 0.000 0.001 0.001 0.000 0.000 0.001 0.000 0.593 0.748 0.000

0.000 0.000 0.880 0.000 0.530 0.000 0.044 0.000 0.873 0.000 0.132 0.000

0.000 0.000 0.005 0.000 0.021 0.000 0.053 0.000 0.004 0.000 0.881 0.000

0.000 0.173 0.000 0.000 0.039 0.000 0.814 0.000 0.253 0.000 0.565 0.000

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Fig. 7. Map of two critical distances (600 m and 2200 m) of housing characteristic influence estimation.

Y.-H. Chen, T. Fik / Applied Geography 80 (2017) 48e63 Table 4 The result of 2005 hedonic model. Variable

Coefficient

t-value

VIF

gro_acre_sr barrier living_sr quality seawall dock acc_beach_log acc_BI_sr dis_STP_log dis_water_sr floodV Adjusted R2

0.4378a 0.2487 a 0.0171 a 0.1030 a 0.0599 a 0.1474 a 0.0730 0.0008 a 0.5768 0.0024 0.1984 a 0.85

14.536 17.958 46.287 9.870 3.378 7.354 6.638 4.939 8.506 10.298 5.762 Number of observation

1.4727 2.7950 1.8363 1.0872 3.7724 4.1189 2.4252 3.2613 1.8638 3.2414 1.0942 2042

Note: a and statistic.

b

a

a a

denote the significance of parameter at 1% and 5% level based on t-

applied: estimation of the original model based on the variables identified in the base-year (2005) result, and estimation of an alternative submarket model which utilized submarket dummy variables. Note that not all of the independent variables tested significant in each model for years other than 2005. Yet among all the variables, gro_acre and living_space were consistently found to be significant at 95% confidence, and had the highest t-values in most of the years examined; thus indicating their importance to determination of market price. Two other variables that always passed the significance tests were acc_beach and barrier. This demonstrated that the distances from houses to public beach accesses and the setting on barrier islands were also critical to the market valuation of housing units. Unlike the 2005 model, flood_V, quality, seawall and acc_BI were variables found not to be consistently significant over the time periods examined. Both flood_V and seawall, which indicate the possibility of being flooded due to a storm surge, were not significant after 2008. This may suggest a change in buyers’ preferences to assume higher risk in the postbubble peak years by not pricing in the effects of vulnerable

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locations. Furthermore, acc_BI was insignificant for a majority of the years in the study period, which denoted that the distances from single family houses to major accesses of barrier islands were not a main consideration to housing buyers. The alternative model with submarket dummy variables verified the existence and substantiality of defining housing submarkets. During the model estimation processes, dis_water was removed to avoid multicollinearity of dis_water and the submarket dummy variables. In addition, the submarket dummy variable for the intermediate submarket (associated with housing units at a distance of 600 to 2200 m from the shoreline) was also eliminated as it did not pass a significant test. This result was most likely due to the fact that this submarket is composed of mixed characteristics, and represents a transitional zone where the market undergoes a gradual change from a coastal to an inland market. While the coastal submarket dummy variable was consistently significant in each of the periods examined; however, the inland submarket dummy variable only passed the significance test in 2003, 2006, and 2008. The results supported the contention the coastal submarket plays a dominant role in overall price of housing within the larger regional market. It was interesting to note that the adjusted R2's of submarket models were higher than the original model. The inclusion of submarket dummy variables had greater explanatory ability than distance to water (dis_water) in the prediction of housing prices. This result suggested that the use of a discrete dummy variables as defined by various critical distances from the shoreline can replace a continuous distance variable to provide better predictions of housing prices. There were no statistically discernible changes of estimated coefficients during the housing-market bubble. This could be explained by the fact that transformations were applied to both dependent and independent variables. Other than dummy variables, most of the variables were transformed by logarithm or square root to maximize the explanatory power of the model. Therefore, most of the coefficient values ranged between 0.6 and 0.6. There were two variables that had marginal trends: gross_area

Table 5 Mean and standard deviation (std.) of variables for hedonic model analysis. variable/year CMV_log (dependent variable) acc_beach (log) acc_BI (sr) barrier dis_STP (log) dis_water (sr) dock flood_V gro_acre (sr) Living (sr) quality seawall coastal submarket inland submarket

mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std. mean std.

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

4.95 0.26 4.26 0.34 111.8 25.09 0.1 0.29 4.94 0.05 35.37 17.56 0.07 0.25 0.01 0.12 0.46 0.09 40.53 9.09 0.17 0.37 0.07 0.26 0.29 0.46 0.18 0.39

4.95 0.27 4.27 0.34 112.74 23.92 0.07 0.26 4.93 0.04 35.85 17.02 0.06 0.24 0.01 0.11 0.47 0.12 40.45 9.07 0.17 0.37 0.06 0.24 0.27 0.45 0.18 0.38

4.94 0.27 4.27 0.3 111.45 24.43 0.08 0.27 4.93 0.04 35.23 16.97 0.07 0.25 0.01 0.1 0.46 0.11 40.19 8.73 0.15 0.35 0.07 0.26 0.28 0.45 0.16 0.37

4.9 0.26 4.28 0.31 113.48 23.84 0.07 0.26 4.94 0.04 35.65 17.03 0.05 0.22 0 0.07 0.45 0.09 39.59 8.03 0.95 0.22 0.06 0.24 0.28 0.45 0.17 0.38

4.92 0.27 4.29 0.32 114.11 23.37 0.06 0.23 4.93 0.04 37.23 16.62 0.05 0.22 0.02 0.13 0.45 0.1 39.27 8.89 0.12 0.32 0.06 0.23 0.24 0.43 0.19 0.39

5 0.27 4.26 0.37 111.48 23.77 0.07 0.26 4.93 0.04 35.19 16.78 0.06 0.23 0.02 0.14 0.47 0.13 40.37 9.6 0.14 0.34 0.07 0.25 0.29 0.45 0.16 0.37

5.04 0.25 4.27 0.29 110.37 23.76 0.07 0.26 4.68 0.15 34.75 16.39 0.07 0.26 0.01 0.09 0.46 0.1 40.28 7.96 0.14 0.35 0.07 0.25 0.28 0.45 0.16 0.37

5.05 0.27 4.25 0.37 111.27 24.88 0.1 0.31 4.94 0.05 34.65 17.94 0.09 0.29 0.01 0.1 0.45 0.1 40.27 8.37 0.15 0.36 0.1 0.3 0.30 0.46 0.18 0.38

5.01 0.27 4.26 0.33 113.22 23.85 0.1 0.3 4.94 0.05 35.35 17.76 0.08 0.27 0.01 0.11 0.46 0.1 40.85 8.43 0.16 0.37 0.09 0.28 0.29 0.46 0.18 0.38

4.99 0.29 4.26 0.37 112.03 23.45 0.08 0.28 4.93 0.05 35.1 17.25 0.08 0.26 0.01 0.11 0.46 0.1 40.65 8.5 0.15 0.36 0.08 0.27 0.29 0.45 0.17 0.38

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Table 6 The coefficient and T-value of 2002e2010 hedonic models with and without submarket dummy. variable/year Intercept acc_beach (log) acc_BI (sr) barrier dis_STP (log) dis_water (sr) dock FloodV gross_acre (sr) living (sr) quality seawall coastal submarket: 100e600 m inland submarket: 2,200 me4,500 m

2002 coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value

adjusted R square

0.7589

variable/year Intercept acc_beach (log) acc_BI (sr) barrier dis_STP (log) dis_water (sr) dock FloodV gross_acre (sr) living (sr) quality seawall coastal submarket: 100e600 m inland submarket: 2,200 me4,500 m adjusted R square

6.560a 15.007 0.043a 2.997 0.0004b 2.077 0.165a 9.516 0.455a 5.021 0.0019a 6.229 0.058b 2.163 0.093a 3.255 0.536a 14.276 0.014a 25.332 0.010 0.920 0.159a 6.730

2003 6.324a 15.425 0.047a 3.270 0.0003 1.539 0.156a 8.811 0.414a 4.839

0.074a 2.777 0.093a 3.279 0.521a 13.951 0.014a 25.367 0.010 0.872 0.157a 6.667 0.056a 6.682 0.027a 3.115 0.7613

2007 coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value coeff. T-value

5.297a 10.508 0.073a 5.085 0.0001 0.544 0.176a 8.483 0.144 1.381 0.0022a 6.308 0.124a 3.945 0.096a 3.529 0.369a 10.764 0.015a 25.537 0.034a 2.511 0.074a 2.697

0.8603

5.692a 13.161 0.044a 3.100 0.0003 1.156 0.175a 9.807 0.272a 3.041 0.0006c 1.917 0.148a 5.429 0.102a 3.330 0.317a 10.703 0.016a 31.148 0.007 0.659 0.103a 3.997

0.77

2004 5.474a 13.382 0.046b 3.229 0.0005a 2.361 0.164a 8.988 0.227a 2.664

0.148a 5.491 0.100a 3.273 0.314a 10.639 0.016a 31.182 0.009 0.843 0.104a 4.063 0.029a 3.487 0.011 1.243 0.7709

2008 4.787a 10.061 0.074a 5.153 0.0002 1.057 0.169a 8.012 0.049 0.490

0.139a 4.463 0.103a 3.774 0.357a 10.407 0.015a 25.711 0.031b 2.279 0.074a 2.713 0.059a 6.059 0.017c 1.722 0.8605

4.257a 25.499 0.096a 6.107 0.0002 0.925 0.158a 8.902 0.080a 2.847 0.0009a 3.127 0.173a 6.256 0.281a 7.878 0.394a 10.854 0.015a 26.248 0.088a 7.752 0.016 0.575

0.8875

6.803a 15.937 0.068a 4.291 0.0001 0.432 0.189a 10.093 0.482a 5.434 0.0016a 5.216 0.144a 5.381 0.047 1.586 0.360a 12.276 0.016a 32.761 0.029a 2.664 0.108a 4.524

0.7709

2005 6.210a 15.440 0.072a 4.537 0.0004b 2.093 0.174a 9.134 0.360a 4.286

0.154a 5.804 0.050c 1.697 0.353a 12.055 0.016a 32.701 0.029a 2.679 0.110a 4.579 0.047a 5.699 0.007 0.862 0.7714

2009 4.039a 24.064 0.093a 6.003 0.0001 0.557 0.147a 8.433 0.116a 4.024

0.165a 6.124 0.279a 7.996 0.384a 10.746 0.015a 26.775 0.080a 7.167 0.027 1.002 0.051a 6.314 0.015c 1.706 0.8913

6.573a 19.937 0.108a 7.304 0.0004b 2.072 0.116a 7.224 0.382a 5.457 0.0015a 5.035 0.196a 6.883 0.019 0.51 0.260a 7.403 0.017a 29.802 0.075a 6.618 0.0370 1.429

0.8855

7.055a 21.548 0.073a 6.638 0.0008a 4.939 0.249a 17.958 0.577a 8.506 0.0024a 10.3 0.147a 7.354 0.198a 5.762 0.438a 14.536 0.017a 46.287 0.103a 9.870 0.060a 3.378

0.8548

2006 6.349a 20.428 0.078a 7.064 0.0003b 2.052 0.234a 16.606 0.437a 6.742

0.165a 8.301 0.201a 5.856 0.422a 14.054 0.017a 46.063 0.103a 9.855 0.059a 3.347 0.066a 10.319 0.012c 1.855 0.8553

2010 6.434a 20.255 0.112a 7.661 0.0003c 1.776 0.110a 6.733 0.359a 5.309

0.207a 7.389 0.019a 0.523 0.255a 7.289 0.016a 29.879 0.073a 6.413 0.035 1.38 0.044a 5.087 0.023a 2.634 0.8867

7.576a 15.561 0.070a 4.396 0.0007a 2.848 0.217a 11.715 0.639a 6.367 0.0023a 6.634 0.033 1.236 0.008 0.242 0.236a 6.722 0.018a 30.17 0.064a 5.259 0.117a 4.974

0.8746

6.764a 14.359 0.129a 8.070 0.0001 0.391 0.177a 8.298 0.407a 4.200 0.0011a 3.225 0.272a 8.754 0.141a 4.786 0.446a 11.644 0.013a 22.783 0.083a 6.385 0.017 0.643

0.8134

6.078a 13.799 0.134a 8.463 0.0005b 2.289 0.151a 7.038 0.262a 2.880

0.274a 8.936 0.136a 4.654 0.438a 11.531 0.013a 23.266 0.075a 5.738 0.018 0.667 0.052a 5.385 0.029a 3.236 0.8167

2011 6.917a 15.098 0.075a 4.660 0.0002 0.792 0.204a 10.768 0.508a 5.344

0.049c 1.857 0.009 0.258 0.220a 6.250 0.018a 30.172 0.063a 5.208 0.120a 5.072 0.062a 6.515 0.011 1.173 0.8748

6.041a 11.890 0.116a 7.410 0.0007a 2.658 0.145a 6.929 0.317a 2.994 0.0022a 5.914 0.114a 3.844 0.021 0.557 0.248a 6.201 0.021a 34.922 0.042a 3.213 0.072a 2.591

0.8691

5.664a 11.868 0.118a 7.654 0.0004 1.512 0.134a 6.362 0.248a 2.480

0.123a 4.203 0.018 0.485 0.238a 6.004 0.021a 35.076 0.037a 2.888 0.077a 2.792 0.070a 7.007 0.017c 1.655 0.8716

Note: a, b and c denote the significance of parameter at 1%, 5% and 10% level based on t-statistic. log denotes logarithm transformation, and sr indicates square root transformation.

had decreasing coefficient values and acc_beach had increasing coefficient values (in terms of absolute values) over the study period. Interestingly, adjusted R2 values showed a distinct trend during the boom-bust cycle. The adjusted R2 increased from 0.75 to 0.88 from 2002 to 2008, and then slightly decreased to 0.86 in 2011, with the adjusted R-square maximized during the base year when all independent variables were shown to be significant (see Fig. 8). The trend of the adjusted R2 also supported the existence of a three-

stage boom-bust cycle as associated with years 2002e2005, 2006e2008, and 2009e2011. During the bubble formation stage (2002e2005), the hedonic models generally had less ability to predict housing prices based on attributes when compared to the bubble peak stage (2006e2008) and the post-bubble stage (2009e2011). Note that the year 2008 marked the turning point from the bubble-peak to the post-bubble years; a year that had the lowest sale numbers but the highest adjusted R2 among the models

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the bubble peak stage, and slightly less during the post-bubble years. Specifically, the average price differences between typical houses on barrier islands versus typical inland houses were estimated at $539,769, $1,834,506, and $1,098,522, respectively, for the three stages identified, which again indicated the difficulty to differentiate coastal submarket from the rest of submarkets during the formation stage of housing-market bubble. 6. Discussion 6.1. Submarket coverage and time stages of the housing-market bubble Fig. 8. Adjusted R-squares of hedonic model analysis. 2005 was selected as the base year to build hedonic models.

Fig. 9. The average housing prices of selected sample houses. A base-10 log scale is applied to y axis.

estimated. To assess the degree to which coastal amenities affect housing prices, the results of pricing estimates for a ‘typical house’ located in different submarkets are shown in Fig. 9. The living and gross area associated with this typical house was fixed e using the mean values of housing units in the entire dataset: more specifically, 1620 square feet and a lot size of 0.214 acres. Other attributes were based on the location of the comparable units. The results demonstrated the extreme nature of price variation within the regional. As expected, a hierarchy of housing submarkets was identified with increasing distance from the shoreline. The highest prices found within the coastal submarket, with the prices decreasing sharply from the beach front houses to barrier islands (away from water) toward mainland locations further inland. Using the 2005 benchmark model, single family housing units located on barrier islands and Flood Zone V had an enormous premium given their near shoreline locations. The average prices of a typical house based on the 2005 model decreased from $1,013,280 (for a typical beach front property), $153,662 (for a house located on a barrier island but further away from the water), to $84,823 (for a typical inland house located on the mainland) with the same physical attributes. Note that the price of an inland housing unit was most likely to be grossly underestimated here given that the units associated with this sub-market cannot be within the coastal storm surge/flood zones. Furthermore, temporal price differences between the coastal and inland submarkets provided an indication of how prices were affect by the various stages of the boom-bust cycles. The differences were smallest during the bubble formation stage, highest during

The housing characteristics influence estimation results showing a consistent spatial pattern of submarket distributions over the study period, which indicates that the coverage of housing submarkets did not experience major changes. In short, the designation of submarkets remained stable throughout the boom-bust cycle. Nevertheless, there was marked temporal variability in the estimated coefficients associated with various housing characteristics. Most of the previous spatio-temporal submarket studies combine multi-year observations into a single hedonic model, using dummy variables to differentiate the effects of the periods (Goodman & Thibodeau, 2008; Pace et al., 1998). While these models do well in accounting for temporal variability in the market as a whole, period-specific local trends and differences in submarket coverage are difficult to assess without incorporating spatial variables. By contrast, this study applied a spatially expansive HCIE model to identify and account for variations of price within and between housing submarkets. Levitin and Wachter (2012) also found three clearly demarcated housing-market bubble stages in their national scale study: 1997e2000, 2001e2003, and 2004e2006. They contend the years 2001e2003 were not part of a housing-market bubble, but simply reflected the impact of relatively high rental prices and low interest rates during the period. They also proposed that 2004e2006 was the height of the U.S. housing market bubble. During this time, the real-estate fundamentals could not support further price increases when interest rates continued to climb. However, the increasing subprime mortgage credits led to the extraordinary rise of housing appreciation; and by 2006, the bubble burst as appreciation rates were not in line with reality. Levitin and Wachter further argued that 2006 was not the end of the housing price collapse, as it was an ongoing process. Krugman (2005) argued that the extravagant housing prices in regional markets along the coasts marked the beginning of a leaky start to the then advancing housing-market bubble. According to Krugman, it was the coastal housing markets that bore the lion's share of the negative economic impacts of the bubble burst. Notwithstanding these macro-level analyses, relatively few studies have focused on the impact of sub-marketspecific adjustments in various stages of the boom-bust cycle. As such, it can be argued that this paper takes on a more comprehensive micro-level approach. The methodology adopted in this paper accounts for temporal variability of the housing market boom-bust cycle while examining localized/spatial effects; in particular, how the determinants of housing prices in various geographic submarkets differ over time and space. Accounting for the spatial aspects of the influence of amenities, such as proximity to shoreline, is critical to understanding of the impact of a market bubble as it runs its course. 6.2. The irrational expectation of housing buyers Previous studies argued that the new constructions and population growth stimulated the real estate market, which in turn, led

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to the housing market bubble (Shimizu & Nishimura, 2007; Smith & Tesarek, 1991). In the Pinellas County, however, two phenomena were noted in census surveys: the population decreased about 1% (Census Bureau, 2000, 2010) and new structure built after 2000 accounted for a small percentage (6.7%) of the total market (Census Bureau, 2012). In addition, very little new land was available for development, since most of the study region had already been developed. These factors indicated that the study area was under a more stable socio-economic climate than other markets, yet it was hit with one of the most extreme housing-market bubbles in the nation. When considering overlooked factors, it is clear that price increases in the study area may have been an outcome of irrational exuberance in a speculative housing market on both the supply and demand sides of the market. Supply-side restrictions in terms of limited new construction, combined with an expectation of high profits from sales led to high turn-over rates as sellers cashed in on the price advances and their equity holdings. On the demand side, the misperception of runaway prices led impulsive buyers to secure homes in a high amenity region before being priced out of the market. Akerlof and Shiller (2009) discussed the irrational expectation of housing buyers during the formation of the nation-wide housing-market bubble on the last decade. During this time, the housing buyers viewed the housing markets as a spectacular opportunity for leverage investment; especially with all the uncertainty over where the stock market might be heading. Braga’s (2009) report “Flip That House” exposed housing flip frauds in Florida, further fueling the extent of the bubble. As one of the ways to leverage investment, house flippers inflated housing prices in order to get higher bank loans; this at a time when banks were willing to loan with little or no money down. Braga also mentioned that some of the inflated value flips became even more excessive after the market got worse in 2005, and as banks continued to fuel the speculative investment frenzy. A hot-spot map of suspicious property flips in Florida was contained reported in Braga's report. The map demonstrated that most of the hot spots for property flips where located in or along coastal communities. After 2005, a large portion of housing investors still believed that housing prices would continue to rise again, which made the flips even higher after the bubble peaked. This condition remained in place up until about September of 2008, when it became clear that the seemingly runaway price trend was over. The coefficient estimates of HCIE model were flatter for the years prior to 2005, with adjusted R-squares that were lower overall e features which suggest that the price of housing units in the coastal submarket were harder to differentiate from those in the entire study region and that housing buyers had less ability to estimate normal market prices based on housing characteristics alone. In short, price levels throughout this period were more an outcome of perceptions and expectations rather than site and situational characteristics of the units themselves. Not surprisingly, our study area was one of the hot spots for housing flip frauds in Braga's property flips map. 7. Conclusion The housing market bubble led to an enormous financial crisis in the U.S. Coastal communities bore the brunt of the bubble impact, as housing prices rose in accordance with demand that was largely driven by irrational exuberance and unfounded perceptions that price would continue to rise beyond the actual underlying worth of the housing stock within the affected submarkets. Studies couched at the community scale can be a key to understanding the formation of housing-market bubbles and the degree to which local housing prices are affected by location, time, and other factors such as amenities. This study aimed to determine the housing price

structure changes in Midwest Pinellas County, Florida, an area that was dramatically affected by the U.S. housing-market bubble. The methods included HCIE and a series of hedonic models that highlight the impacts within various and identifiable housing submarkets over time and space. Two consistent changes in estimated coefficients were identified in the HCIE results. Observed critical distances were found at 600 m and 2200 m from the shoreline; revealing the existing of three housing submarkets: a coastal market, an intermediate market (transition zone), and an inland submarket. Consistency in the identification of the critical distances across models and estimated coefficients suggested that the coverage of each submarket was stable throughout the boom-bust cycle. However, variations in the estimated coefficients across time indicated the value of the housing characteristics changed as the market bubble ran its course. Furthermore, evidence of three distinct time stages were observed based on the temporal trends in the estimated coefficients: signaling a bubble-formation stage from 2002 to 2005, a bubble-peak stage from 2006 to 2008, and post-bubble stage from 2009 to 2011. The hedonic models run on individual years of the boom-bust cycle (2002e2011) suggest a complexity in the structure of housing prices over time and space, and non-linear relationships between prices and determinants. Hedonic models with submarket dummy variables had a greater explanatory power in terms of predicting housing prices. These models out-performed specifications where distance from the coast or shoreline was included purely as a distance variable. Trends in the adjusted R2 values (lower R-squares) during the bubble formation and peak stages, suggested that prices were less driven by housing characteristics and more likely an outcome of market signals tied to irrational expectations and perceived price trends by sellers and buyers. Myers and Ryu (2008) proposed that the “baby boomers” were the main age-cohort responsible for initiating the bubble. In their analysis, Florida baby boomers (age 65e69) had the greatest propensity to buy or sell houses. Further analysis can be done by combining buyers' profiles and/or socio-economic characteristics directly into the hedonic model to see test for differences in the willingness to pay for various housing attributes among socioeconomic/demographic groups, an approach taken by Kestens, Theriault, and Rosiers (2006); to see if buyer characteristics helped explain variability in the selling price of housing. The methodology developed in this study can also be applied to other high amenity study areas; for instance, areas in growing cities that are experiencing employment and housing booms. The results can be used to gain insights into the degrees of housing buyers’ expectations across various geographic markets in high-growth bubbleprone areas, and the degree to which prices are affected by various urban and recreational amenities across sub-markets. References Akerlof, G. A., & Shiller, R. J. (2009). Animal spirits : How human psychology drives the economy, and why it matters for global capitalism. Princeton, NJ: Princeton University Press. Baker, D. (2008). The housing bubble and the finicial crisis. real-world economics review, 46, 73e81. Baranzini, A., Ramirez, J., Schaerer, C., & Thalmann, P. (2008). Hedonic methods in housing markets - pricing environmental amenities and segregation. New York: Springer ScienceþBusiness Media, LLC. Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17(1), 61e85. Benson, E. D., Hansen, J. L., Schwartz, A. L. J., & Smersh, G. T. (1998). Pricing Residential Amenities: The Value of a View. Journal of Real Estate Finance and Economics, 16(1), 55e73. Bourassa, S. C., Hoesli, M., & Peng, V. S. (2003). Do housing submarkets really matter? Journal of Housing Economics, 12(1), 12e28. Braga, M. (2009). 'Flip that house' fraud cost billions. In Herald-tribune. Sarasota, FL: Sarasota Herald-Tribune.

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