Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach
Abstract
:1. Introduction
2. Background on the Main Mass Appraisal Techniques
3. Case Studies
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- The total surface of the property, expressed in square meters of gross floor area of the property [S];
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- The number of the bathrooms in the property [B];
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- The floor on which the property is located [F];
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- The presence of the lift [L]. In the model, this variable is considered as a dummy variable, in particular the presence of the service is represented by the value “one”, whereas the absence of the service is indicated with the value “zero”;
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- The quality of the maintenance condition of the apartment, taken as a qualitative variable and differentiated, through a synthetic evaluation, by the categories “to be restructured” [Mp], “good” [Mg], and “excellent” [Me]. Following the logic of the dummy variables, the score “one” is assigned to the category that defines the specific quality of each property, and the score “zero” for the remaining two categories [110]. In particular, the “to be restructured” state refers to properties that require significant refurbishment interventions, due to the fact that the functionality of the property is compromised by the inappropriate conditions of the elements that compose it; the “good” state indicates properties whose maintenance conditions are acceptable and whose functions can be conducted without heavy interventions. Finally, the “excellent” state refers to buildings characterized by construction and aesthetic high quality, possibly affected by recent redevelopment and renovation initiatives;
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- The energy performance certificate (EPC) label [Ep], expressed, according with the current regulations, through the denominations from A4 (the highest level) to G (the lowest level). In the present research, the EPC labels from A4 to A are gathered into a single explanatory variable (EpA). Therefore, the variables considered are specified by the following abbreviations, which recall the label they belong to: EpA, EpB, EpC, EpD, EpE, EpF, EpG. Each parameter is interpreted as a dummy variable, assigning a score equal to “one” to the EPC label of the property and, consequently, the score equal to “zero” to all the others;
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- The age of the building in which the residential unit is located [O]. This variable is calculated as the difference between the year when the property was sold (2016–2017) and the year of construction of the building.
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- The distance from the nearest highway, expressed in kilometers it takes to get there by car [T] (determined through the application on www.google.com/maps);
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- The distance from the nearest subway, expressed in kilometers it takes to walk to it [W] (determined through the application on www.google.com/maps);
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- The municipal trade area in which the property is located, considering the geographical distribution developed by the Italian Revenue Agency (http://www.agenziaentrate.gov.it), due to the different location characteristics that contribute to the formation of the selling prices. In particular, five trade areas are defined by the Italian Revenue Agency: “central”, “semi-central”, “peripheral”, “suburban”, and “rural”. With regard to the cities under analysis, the Italian Revenue Agency considers four trade areas: “central” [Uc], “semi-central” [Usc], “peripheral” [Up], and “suburban” [Usb]. For each property, the score “one” is assigned if the property belongs to the specific trade area, whereas the score “zero” is reported for all the remaining spatial factors.
4. The Method
5. Application of the Multi-Case Strategy for EPR Method
5.1. The Generalized Model Obtained and Its Specification to the Case Studies
5.2. Empirical Analysis of the Functional Relationships in Each City Model
6. Comparison with the Hedonic Price Method
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Variable | Acronym | Type of Variable | Measure |
---|---|---|---|
Unit selling price | P | cardinal | €/m2 |
Technical factors | |||
Floor surface | S | cardinal | m2 |
Number of bathrooms | B | cardinal | number |
Floor | F | cardinal | number |
Presence of lift | L | dummy | 1—presence, 0—absence |
Quality of the maintenance condition of the apartment | Mp | dummy | 1—category that defines the specific quality of each property, 0—the remaining two categories |
Mg | |||
Me | |||
EPC label | EpA | dummy | 1—EPC label of the property, 0—all the others |
EpB | |||
EpC | |||
EpD | |||
EpE | |||
EpF | |||
EpG | |||
Age of the building | O | cardinal | Number—difference between the year of sale and the construction year of the building |
Spatial factors | |||
Distance from the nearest highway | T | cardinal | km by car |
Distance from the nearest subway | W | cardinal | km by walking |
Municipal zone | Uc | dummy | 1—if the property belongs to the specific trade area, 0—all the remaining spatial factors |
Usc | |||
Up | |||
Usb |
Variable | Mean | Standard Deviation | Levels/Intervals | Frequency |
---|---|---|---|---|
Unit selling price [€/m2] | 2244 | 812 | ||
Technical factors | ||||
Floor surface [m2] | 99.08 | 48.27 | ||
<50 | 0.105 | |||
50–70 | 0.19 | |||
70–110 | 0.30 | |||
110–150 | 0.26 | |||
>150 | 0.145 | |||
Number of bathrooms [n.] | 1.485 | 0.558 | ||
1 | 0.545 | |||
2 | 0.425 | |||
3 | 0.03 | |||
Floor [n.] | 2.585 | 2.264 | ||
0 | 0.12 | |||
1 | 0.335 | |||
2 | 0.10 | |||
3 | 0.165 | |||
4 | 0.085 | |||
5 | 0.065 | |||
>5 | 0.13 | |||
Presence of lift [1—presence, 0—absence] | 0.72 | 0.45 | ||
0 | 0.28 | |||
1 | 0.72 | |||
Quality of the maintenance condition | To be restructured | 0.53 | ||
Good | 0.29 | |||
Excellent | 0.18 | |||
EPC label | A | 0.155 | ||
B | 0.08 | |||
C | 0.105 | |||
D | 0.12 | |||
E | 0.085 | |||
F | 0.16 | |||
G | 0.295 | |||
Age of the building [difference between the year of sale and the construction year of the building] | 42.51 | 2.52 | ||
<10 | 0.285 | |||
10–30 | 0.11 | |||
30–50 | 0.19 | |||
>50 | 0.415 | |||
Spatial factors | ||||
Distance from the nearest highway [km by car] | 2.775 | 1.409 | ||
<2 | 0.315 | |||
2–3 | 0.265 | |||
3–4 | 0.18 | |||
>4 | 0.24 | |||
Distance from the nearest subway [km by walking] | 2.876 | 2.338 | ||
<1 | 0.255 | |||
1–2 | 0.21 | |||
2–6 | 0.395 | |||
>6 | 0.14 | |||
Municipal zone | Central | 0.505 | ||
Semi-central | 0.17 | |||
Peripheral | 0.11 | |||
Suburban | 0.215 |
Variable | Mean | Standard Deviation | Levels/Intervals | Frequency |
---|---|---|---|---|
Unit selling price [€/m2] | 2992 | 1569 | ||
Technical factors | ||||
Floor surface [m2] | 107 | 52.02 | ||
<50 | 0.115 | |||
50–70 | 0.12 | |||
70–110 | 0.39 | |||
110–150 | 0.245 | |||
>150 | 0.13 | |||
Number of bathrooms [n.] | 1.57 | 0.64 | ||
1 | 0.51 | |||
2 | 0.405 | |||
3 | 0.085 | |||
Floor [n.] | 2.46 | 1.85 | ||
0 | 0.095 | |||
1 | 0.27 | |||
2 | 0.205 | |||
3 | 0.175 | |||
4 | 0.115 | |||
5 | 0.075 | |||
>5 | 0.065 | |||
Presence of lift [1—presence, 0—absence] | 0.70 | 0.46 | ||
0 | 0.3 | |||
1 | 0.7 | |||
Quality of the maintenance condition | To be restructured | 0.305 | ||
Good | 0.335 | |||
Excellent | 0.36 | |||
EPC label | A | 0.185 | ||
B | 0.045 | |||
C | 0.085 | |||
D | 0.055 | |||
E | 0.11 | |||
F | 0.255 | |||
G | 0.265 | |||
Age of the building [difference between the year of sale and the construction year of the building] | 70.50 | 57.25 | ||
<10 | 0.14 | |||
10–50 | 0.24 | |||
50–80 | 0.335 | |||
>80 | 0.285 | |||
Spatial factors | ||||
Distance from the nearest highway [km by car] | 3.29 | 1.52 | ||
<2 | 0.19 | |||
2–3 | 0.35 | |||
3–4 | 0.225 | |||
>4 | 0.235 | |||
Distance from the nearest subway [km by walking] | 1.20 | 1.04 | ||
<0.5 | 0.295 | |||
0.5–1 | 0.315 | |||
1–3 | 0.335 | |||
>3 | 0.055 | |||
Municipal zone | Central | 0.34 | ||
Semi-central | 0.35 | |||
Peripheral | 0.135 | |||
Suburban | 0.175 |
Variable | Mean | Standard Deviation | Levels/Intervals | Frequency |
---|---|---|---|---|
Unit selling price [€/m2] | 2468 | 1316 | ||
Technical factors | ||||
Floor surface [m2] | 79.39 | 44.33 | ||
<50 | 0.27 | |||
50–70 | 0.295 | |||
70–110 | 0.27 | |||
110–150 | 0.105 | |||
>150 | 0.06 | |||
Number of bathrooms [n.] | 1.21 | 0.45 | ||
1 | 0.81 | |||
2 | 0.17 | |||
3 | 0.02 | |||
Floor [n.] | 2.41 | 1.64 | ||
0 | 0.045 | |||
1 | 0.335 | |||
2 | 0.19 | |||
3 | 0.215 | |||
4 | 0.095 | |||
5 | 0.09 | |||
>5 | 0.03 | |||
Presence of lift [1—presence, 0—absence] | 0.75 | 0.44 | ||
0 | 0.255 | |||
1 | 0.745 | |||
Quality of the maintenance condition | To be restructured | 0.23 | ||
Good | 0.29 | |||
Excellent | 0.48 | |||
EPC label | A | 0.235 | ||
B | 0.055 | |||
C | 0.17 | |||
D | 0.22 | |||
E | 0.11 | |||
F | 0.075 | |||
G | 0.135 | |||
Age of the building [difference between the year of sale and the construction year of the building] | 83.25 | 74.03 | ||
<10 | 0.14 | |||
10–50 | 0.24 | |||
50–80 | 0.33 | |||
>80 | 0.29 | |||
Spatial factors | ||||
Distance from the nearest highway [km by car] | 4.99 | 1.38 | ||
<3 | 0.13 | |||
3–5 | 0.345 | |||
5–6 | 0.275 | |||
>6 | 0.25 | |||
Distance from the nearest subway [km by walking] | 1.57 | 1.15 | ||
<0.5 | 0.235 | |||
0.5–1 | 0.185 | |||
1–3 | 0.465 | |||
>3 | 0.115 | |||
Municipal zone | Central | 0.30 | ||
Semi-central | 0.48 | |||
Peripheral | 0.22 | |||
Suburban | 0.00 |
Polynomial Expression Structure | |
Inner Function f | No function f |
Modeling Type | Statical Regression |
Maximum Number of Terms | 10 |
Exponents | [−2, −1.5, −1, −0.5, 0, 0.5, 1, 1.5, 2] |
Regression Method | LS (Least Squares) |
Equation (n) | Model | CODMCS [%] |
---|---|---|
(3) | 60.00 | |
(4) | 63.78 | |
(5) | 65.89 | |
(6) | 66.13 | |
(7) | 69.60 | |
(8) | 70.87 | |
(9) | 71.28 | |
(10) | 72.34 | |
(11) | 78.32 |
a0 | a1 | a2 | a3 | a4 | a5 | a6 | a7 | a8 | a9 | a10 | |
---|---|---|---|---|---|---|---|---|---|---|---|
BARI | 7.59 | 0 | 0 | −0.49 | 0.23 | 0.24 | 0 | 0.14 | 0.16 | −0.0006 | 0 |
ROME | 7.42 | 0.30 | 0 | 0 | 0 | 0 | −0.06 | 0.27 | 0.37 | −0.0009 | −3.8E-05 |
TURIN | 5.50 | 0.28 | 0.77 | 0 | 0 | 0.38 | 0 | 0 | 0.26 | 0 | 0 |
Model | RMSE [%] | MAPE [%] | MaxAPE [%] | |
---|---|---|---|---|
BARI | 4.03 | 2.41 | 9.62 | |
ROME | 3.89 | 3.02 | 8.37 | |
TURIN | 4.15 | 2.58 | 8.24 |
BARI | ROME | TURIN | ||||
---|---|---|---|---|---|---|
Training Set | Validation Set | Training Set | Validation Set | Training Set | Validation Set | |
Iteration 1 | 2.545 | 2.985 | 2.985 | 3.254 | 3.021 | 3.245 |
Iteration 2 | 2.816 | 2.657 | 2.835 | 3.327 | 2.854 | 2.745 |
Iteration 3 | 3.120 | 3.247 | 3.021 | 2.989 | 2.968 | 3.040 |
Iteration 4 | 3.058 | 3.049 | 3.254 | 3.654 | 2.476 | 2.847 |
Iteration 5 | 2.459 | 2.843 | 3.115 | 3.018 | 3.124 | 2.980 |
Iteration 6 | 2.847 | 2.642 | 3.541 | 3.899 | 3.016 | 3.124 |
Iteration 7 | 2.012 | 2.451 | 3.095 | 3.367 | 2.012 | 2.743 |
Iteration 8 | 2.267 | 2.897 | 2.999 | 3.253 | 2.915 | 2.802 |
Iteration 9 | 3.196 | 3.074 | 3.001 | 2.899 | 3.047 | 3.000 |
Iteration 10 | 3.087 | 3.547 | 3.152 | 3.473 | 2.682 | 3.086 |
S | B | F | L | Mp | Me | EpA | EpG | O | T | W | Uc | Usb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BARI | |||||||||||||
ROME | |||||||||||||
TURIN |
Variable | Coefficient | Significance |
---|---|---|
constant | 7.5181 | *** |
S | −0.0006 | - |
B | 0.0108 | - |
F | 0.0136 | - |
L | 0.2304 | *** |
Me | 0.2479 | *** |
EpA | 0.2127 | *** |
EpG | −0.2736 | *** |
W | −0.0403 | *** |
Variable | Coefficient | Significance |
---|---|---|
constant | 7.2210 | *** |
S | 0.0008 | - |
B | 0.0810 | - |
F | 0.0338 | ** |
L | 0.4320 | *** |
Mp | −0.0652 | - |
EpG | −0.0607 | - |
W | 0.1160 | *** |
O | −0.0009 | * |
Uc | 0.2697 | *** |
Usb | −0.5254 | *** |
Variable | Coefficient | Significance |
---|---|---|
constant | 6.1522 | *** |
B | 0.1196 | *** |
L | 0.2630 | *** |
Me | 0.3748 | *** |
T | 0.1840 | *** |
Uc | 0.2684 | *** |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Morano, P.; Rosato, P.; Tajani, F.; Manganelli, B.; Di Liddo, F. Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach. Sustainability 2019, 11, 4896. https://doi.org/10.3390/su11184896
Morano P, Rosato P, Tajani F, Manganelli B, Di Liddo F. Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach. Sustainability. 2019; 11(18):4896. https://doi.org/10.3390/su11184896
Chicago/Turabian StyleMorano, Pierluigi, Paolo Rosato, Francesco Tajani, Benedetto Manganelli, and Felicia Di Liddo. 2019. "Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach" Sustainability 11, no. 18: 4896. https://doi.org/10.3390/su11184896