# Does Urban Green Infrastructure Increase the Property Value? The Example of Magdeburg, Germany

^{*}

## Abstract

**:**

## 1. Introduction

^{2}are the respective purchase price collections of the expert committees. With the help of conversion coefficients and value factors, properties with different buildings and uses can be adapted to a selected sample property [5]. In the urban area, there are a number of examples that demonstrate the upgrading of the property quality and, thus, also the increase in the property value (via the SLRV) after the construction of green infrastructure. To date, however, the published scientific literature on this topic is scarce. The research questions related to urban greenery and land value are comparable on a global scale: Who benefits from the existing impact of green space on real estate market values? Who benefits from the usually positive influence: private or public property owners, e.g., the green space authorities? Does the community generally pay for the initial setup, care and maintenance of green spaces, and due to this favorable location does the private owner have better chances of renting at a higher rent and selling at a higher market price? How do SLRV develop in green areas compared to ecologically poorly equipped areas?

^{2}per m

^{2}leaf area index caused a gain of 1.4 life years in the context of cardio-metabolic conditions [10]. GI planning also includes the restoration of degraded ecosystems [6]. Ecosystem restoration needs to be supported by predicting habitat evolution during restoration activities. Typically, such a type of model takes into account two steps: (a) the reference state and (b) the ecological direction, i.e., the diversity of life forms present, the structure of the species to be created and the role that these species play in the ecological system [11,12]. Well-functioning ecosystems can provide a constant flow of ecosystem services that are central to human well-being [10]. A comprehensive overall assessment of ecosystem services and their costs and benefits is indicated in planning decision-making processes, so that ecosystems are protected and harmful interventions prevented by quantifying the ecological value [13].

^{2}for the greenery areas compared to other surrounding mixed areas (Halle 180%, Aschersleben 5%). Also in Halle City, the property value increased since 2016. In Aschersleben, the values are generally lower than in the two major cities, which reflects the general situation in a structurally weak region. The State Garden Show 2010 in Aschersleben, which brought about structural and horticultural changes in the city, did not immediately lead to an increase in value, which can probably be attributed to the effects of the financial crisis of 2008/2009. Further investigations for Germany were performed by Ali et al. (2020) [26]. They investigated gentrification through green regeneration in the Lene-Voigt-Park in Leipzig, Eastern Germany. Bokarjova et al. (2020) [27] and Rigolon and Németh (2018) [28] confirmed this subject as well. On the international level, I-Chun Tsai et al. (2023) [29] focused the influence of migration policy risk on market segmentation of housing and rental markets in the Euro Area. Moreover, compared with housing market connectedness, the rental market connectedness is less influenced by migration policy risk and migration fear in the UK than in Germany [29]. The findings of Mell et al. (2016) [30] and Dell’Anna et al. (2022) [31] support the wider literature evaluating the economic value of GI which argues that investment in urban greenspace can have a significant impact on local housing and commercial markets where it produces more attractive and functional landscapes. The objective of this study was to investigate the relationship between financial, socioeconomic and urban ecosystems providing ecosystem services, on SLRV. For this purpose, a principal component analysis was used to assign the selected indicators to the corresponding components. A multiple regression was used to discuss the impacts on SLRV. For this research, a case study was conducted for the city of Magdeburg, Saxony-Anhalt, Germany.

## 2. Materials and Methods

^{2}, 60 m

^{2}and 100 m

^{2}). Here, y is the searched OCR at time x (year; Y). The following Equations (1)–(5) are used for OCR:

_{30,i}= 0.1236 × Y − 243.0481 ± 0.20 EUR/m

^{2},

_{60,i}= 0.1301 × Y − 256.8140 ± 0.09 EUR/m

^{2},

_{100,i}= 0.1825 × Y − 361.7568 ± 0.29 EUR/m

^{2},

_{i}= (OCR

_{30,i}+ OCR

_{60,i}+ OCR

_{100,i})/3

_{m,i}= 0.1454 × Y − 287.2063 ± 0.17 EUR/m

^{2}.

_{m}is the estimation function for the mean values with equal distribution of the different apartment sizes, which is rarely the case. Accordingly, a correction factor was introduced for each of the different SLRVs, which considers the inhomogeneous distribution of housing supply. Equation (6) shows the determination of the correction factor.

_{c,j}= OCR

_{j,2020}/OCR

_{m,2020}

_{m,j,i}= f

_{c,j}× OCR

_{m,i}

_{j}= E

_{j}× INCa

_{j}

_{SLVR,}see Table 1). Furthermore, the density of persons and persons with migration background (P) are obtained.

_{i}or IMIG

_{i}= P

_{i,j,k}/A

_{SLVR,j}

^{2}per tree individual. In general, leaf area differs among tree species. We reduced these individual allometric growth conditions to a general approach. The tree individuals were determined for each area studied. After that, the crown diameter (cd) and tree height (h) were chosen to approach the leaf area per tree individual (indices t). According to Fauk and Schneider [44], the individual growth of trees has many influencing factors. These complex interactions between biotic and abiotic environment cannot be considered in this study. However, we used the knowledge about the general growth conditions for urban trees in the city of Magdeburg to introduce a simple model for crown expansion and height growth. We assumed an annual crown diameter growth of 0.5 cm and a height increase of about 1 cm. These values are to be considered conventional. Equation (10) shows the approach used.

_{i,j,t}= [500 × (1 − exp(−((cd − (0.005 × ∆Y)) × ((h − (0.01 × ∆Y))/3)/100))) + 1]

_{i,j}= ∑LA

_{i,j,t}

_{i,j}= LA

_{i,j}/A

_{SLVR,i,j}

_{j,i}. Ultimately, the Shannon Index was calculated as a measure of plant diversity as follows:

_{j,i =}−∑ p

_{j,i}× ln(p

_{j,i})

_{j,i}/ln(S)

^{2}. For this purpose, the raster calculator and the zone statistics of the QGIS software were used and the data was transferred to the residential areas. Finally, the calculation of the GS proportion was carried out per inhabitants for the respective residential area and is shown in Equation (15).

_{j}= (A

_{GI,j}/∑P

_{j,k})

**H**

_{0}**H**

_{1}:^{2}) was used as a test variable for assessment. The significance level was determined from the sample size (N), the number of variables (v) and the determinants of the correlation matrix (det(R)) using the degree of freedom (df = (v × (v − 1))/2).

^{2}= −ln(det(R)) × (N − 1 − (2 × v + 5)/6)

^{0.5}

_{m}) are considered.

_{s}= ∑

_{i}(R(x

_{i}) − R

_{m,x}) × (R(y

_{i}) − R

_{m,y})/[(∑

_{i}(R(x

_{i}) − R

_{m,x})

^{2})

^{0.5}× (∑

_{i}(R(y

_{i}) − R

_{m,y})

^{2})

^{0.5}]

_{p}= ∑

_{i}(X

_{i}− X

_{m}) × (Y

_{i}− Y

_{m})/[(∑

_{i}(X

_{i}− X

_{m})

^{2})

^{0.5}× (∑

_{i}(Y

_{i}− Y

_{m})

^{2})

^{0.5}]

_{i}∑

_{i’}(r

_{xixi’})

^{2}/[∑

_{i}∑

_{i’}(r

_{xixi’})

^{2}+ ∑

_{i}∑

_{i’}partial_(r

_{xixi’})

^{2}]

_{i’}(r

_{xixi’})

^{2}/[∑

_{i’}(r

_{xixi’})

^{2}+ ∑

_{i’}partial_(r

_{xixi’})

^{2}]

_{xixi’})) is defined as a correlation between two variables with no further influence from the other variables [48].

_{1}I

_{i}+ c

_{2}I

_{i+1}+ … + c

_{n}I

_{i+n}

_{1}= b + m × SLV

_{2}= b + m × SLV + n × SLV

^{2}

## 3. Results

#### 3.1. Results of Data Matrix Test and Data Refinement Procedure

^{2}(190) = 6667.64, p < 0.001), iteration 8 (X

^{2}(66) = 2099.27, p < 0.001)). These data are probably suitable for factor analysis. A singular matrix was reported for the rank correlation (Kendall, Spearman). This indicates correlated values in the initial matrix. Accordingly, the further procedure (KMO and MSA) was based on Pearson’s method, while Kendall’s and Spearman’s methods were not considered further. Since Bartlett’s test only shows the principal suitability of the data set, the Kaiser–Meyer–Olkin criterion with the respective MSAs served to fit the data set. Each exclusion of a variable can have a large effect on KMO and MSA, only single variables (indicators) were removed. The initial data set had a KMO value of 0.374 and was classified as unacceptable. Based on the MSA criterion, min_gi (0.160) was removed. For iteration 1, there was an increase in KMO to 0.407 (unacceptable). The indicator BDI (0.145) was excluded. Iterations 2 (KMO = 0.395 and MSA excl. = ret (0.163)), 3 (KMO = 0.368 and MSA excl. = tra (0.203)) and 4 (KMO = 0.466 and MSA excl. = LAI (0.284)) were also unacceptable. The first iteration to show a miserable result for the KMO value (0.562) was number 5. Evenness was excluded based on the MSA of 0.301. Analogous to iteration 5, iteration 6 also showed a miserable KMO score (0.595). Based on the lowest MSA, Water (0.382) was extracted. Iteration 7 and 8 were both mediocre (Iter. 7 = 0.641 and Iter. 8 = 0.661). After iteration 7, IMIG (0.483) was removed. Iteration 8 indicated SLRV (0.495) as an indicator to be extracted, so the final data set was reached at this point. The full results are given in Table 2.

#### 3.2. Evaluation of the PC Analysis

^{2}was used after reducing the dimensions. The initial h

^{2}for SLRV was 1 (normal for PCA analysis). After reducing the dimensions, the commonality was 0.8220. h

^{2}for all other indicators was greater than 0.5 and the absolute minimal score in one of the three dimensions was greater than 0.5. All in all, no further indicator exclusion was performed by the PCA analysis. In the case of a PCA, the indicators shown in green would each have to be assigned to the dimensions individually. Dimension 1 showed that the standard land value could apparently only be described by social factors and the green infrastructure. However, the communality for SLRV was then only 0.4530 (45.30% explained variance of the variable SLRV). In the second dimension, only loadings of the financial indicators greater than 0.5 were evident, and an absolute loading greater than 0.3 was still recorded for SLRV. We do not exclude SLRV and with the dimension 2, we obtain a communality of 0.6124. This 61.24% explained variance was not enough for us either. Furthermore, the number of trees would have no influence at this point and this variable would be described incompletely (h

^{2}= 0.3848). With the dimension 3, on the one hand, a significant part of the communality for SLRV (h

^{2}= 0.8220) could be described; on the other hand, the contribution of the city trees was identified by an absolute loading >0.5. The SLRV charge was close to 0.5, which was why a connection had to be assumed. The fourth dimension can be largely ruled out, (a) no significant loading of the variable SLRV, (b) double loading of School, which is why it was already described in the previous dimension. The PCA analysis showed that initially all 12 variables should be used for further multiple regression. The PCA scores are shown in the Table 3 for three Dimensions. The full PCA result can be seen in the Table 4.

#### 3.3. Results of the Multiple Regression

^{2}. Now, another improvement of the model has been made. For this purpose, linear and quadratic functions were used. The linear function could not improve the multiple correlation coefficient (r = 0.9192), but the model quality improved from adj. r

^{2}= 0.8297 to adj. r

^{2}= 8440. The RMSE was EUR 30.12 per m

^{2}. The quadratic equation for model fitting yielded an improvement in the multiple correlation coefficient (r = 0.9504) and that of the model goodness (r

^{2}= 0.9019). The RMSE was EUR 23.80 per m

^{2}. The coefficients, as well as the parameter statistics, can be found in the Table 6 and Table 7. The graphical illustration can be seen in the Figure 9 and Figure 10. The comparison of the individual models was realized in the Figure 11.

#### 3.4. Relationship between the Standard Land Values and Components of the Green Infrastructure

^{2}. Meanwhile, an area with 830 trees was worth less by an estimated −25.19 (CI 95%: −45.16–1.75) EUR per m

^{2}. Simplified, it can be stated that the SLRV per percentage change from the average of trees individuals decreases by an average of 0.11 (CI 95%: −0.54–0.36) EUR per m

^{2}. Furthermore, a change of −52.5% in the number of trees caused a rising of 3.9 (CI 95%: −9.1–15.9) % in the SLRV. Likewise, an increase in the number of trees about 51.2% causes a decrease in SLRV of −3.6 (CI 95%: −17.1–10.7) %. Thus, the past 14 years indicate that in the evaluation of standard land values, urban trees as an important service provider for human health are considered in contrary contradiction or underrepresented. The result of the scenario “tree planting” was presented with the main parameters in the Figure 12 and with the 95% confidence interval in the Figure 13.

^{2}× %). The distance to the allotments ranged from 196 to 916 m in our data set, with an average of 421 m distance (crow flies). Thus, 195 m to an allotment garden site causes a reduction in the standard land value by 21.62 (CI 95%: −31.72–−14.30) EUR per m

^{2}. While an increase in the path distance to 990 m means that about 74.74 (CI 95%: 16.75–148.16) EUR per m

^{2}more can be achieved. Furthermore, a change of −53.7% in the distance to the allotment sites caused a reduction of 14.2 (CI 95%: −20.8–−9.4) % in the SLRV. Likewise, an increase in the distance to allotment gardens of 53.2% causes an increase in SLRV of 17.0 (CI 95%: −4.6–41.6) %. The result of the scenario “land transformation allotment garden” was presented with the main parameters in the Figure 14 and with the 95% confidence interval in the Figure 15.

## 4. Discussion

^{2}× %). This may well be methodical, as allotment garden sites are readily used by ecological lending in planning offices as well as local government for land development. Despite all the discussion about species extinction, loss of genetic diversity and formation of resilience for the urban ecosystem, it is not the monocultural agricultural land with almost no benefit, except for a food production, but important urban ecosystems that are required of great importance for sustainable design that are decimated. Now, if the adjacent area is “favorable”, after the land use change from allotments to settlement area, the SLRV of the adjacent settlement area is used.

## 5. Conclusions

_{2}binding potential, soil resources (e.g., for water retention in the area), fine dust binding potential and biomass resources represent criteria with which a quantitative upgrading of the areas can be balanced, and should be subject to future investigations.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Areas of interest, City of Magdeburg. © https://www.openstreetmap.org and contributors.

**Figure 3.**Methodological approach for the identification of linear relationships between the indicators under study; Note: the Kaiser–Meyer–Olkin criterion must have at least “mediocre” in min one of the regression methods (Spearman, Pearson, Kendall). Better scores (e.g., middling, meritorious) are explicitly desirable; the MSA must be greater or equal 0.5; the green arrows indicate the steps towards the result, and the red arrows indicate preparatory/corrective steps; the linkage of the initial data set with the regression results (to illustrate the effects) is symbolized with the black dashed arrow.

**Figure 7.**Presentation of Model 1 (without adjustment) based on the standardized data for the corresponding residential areas (RAi, see Figure 1).

**Figure 8.**Presentation of Model 1 (without adjustment) based on the back-transformed data for the corresponding residential areas in EUR per m

^{2}.

**Figure 9.**Presentation of Model 2 (with linear Model fitting) based on the back-transformed data for the corresponding residential areas in EUR per m

^{2}.

**Figure 10.**Presentation of Model 3 (with quadratic Model fitting) based on the back-transformed data for the corresponding residential areas in EUR per m

^{2}.

**Figure 11.**Presentation of the Model comparison, based on the back-transformed data for the corresponding residential areas in EUR per m

^{2}.

**Figure 12.**Presentation of the relationships between the number of street trees and standard land value. Explanation of the “difference of trees to the mean number of trees” (bar chart): increase in tree individuals compared to the mean (green), decrease in tree individuals compared to the mean (red).

**Figure 13.**Presentation of the relationships between the number of street trees and standard land value with confidence interval.

**Figure 14.**Presentation of the relationships between the distance to allotment gardens and standard land value. Explanation of the “difference of distance to the mean distance to allotments” (bar chart): increase in the distance to the nearest allotment garden compared to the mean (green), decrease in the distance to the nearest allotment garden compared to the mean (red).

**Figure 15.**Presentation of the relationships between the distance to allotment gardens and standard land value with confidence interval.

**Figure 16.**(

**A**) Effect of trees for 2006, zero Model, 2020; (

**B**) Effect of distance to allotments for 2006, zero Model, 2020 on the SLRV.

**Table 1.**The district, with the corresponding residential areas with the entire areal area and green areas based on the normalized difference vegetation index NDVI.

District | Residential Area j | Total Area in ha A _{SLVR} | Area of Green Spaces in ha A _{GI} |
---|---|---|---|

Stadtfeld Ost | RA 1 | 12.5 | 4.0 |

Stadtfeld Ost | RA 2 | 80.0 | 28.6 |

Stadtfeld Ost | RA 3 | 12.1 | 6.9 |

Stadtfeld Ost | RA 4 | 102.2 | 30.0 |

Stadtfeld West | RA 5 | 98.6 | 47.8 |

Stadtfeld West | RA 6 | 18.6 | 9.4 |

Stadtfeld West | RA 7 | 9.7 | 5.5 |

Stadtfeld West | RA 8 | 32.1 | 19.5 |

Stadtfeld West | RA 9 | 8.7 | 4.6 |

Stadtfeld West | RA 10 | 16.1 | 9.8 |

Altstadt | RA 11 | 38.7 | 15.3 |

Altstadt | RA 12 | 4.1 | 1.8 |

**Table 2.**The test results for the MSA test with regression method Pearson for each indicator and iteration. Red = deleted variable, wavy undersigned = Indicator of Interest (<0.5); Italic: KMO value of the respective iteration.

Design. | Start | Iter. 1 | Iter. 2 | Iter. 3 | Iter. 4 | Iter. 5 | Iter. 6 | Iter. 7 | Iter. 8 |
---|---|---|---|---|---|---|---|---|---|

SLRV | 0.577 | 0.632 | 0.568 | 0.533 | 0.464 | 0.557 | 0.564 | 0.566 | 0.495 |

INH | 0.518 | 0.546 | 0.382 | 0.362 | 0.463 | 0.639 | 0.633 | 0.670 | 0.675 |

IMIG | 0.655 | 0.584 | 0.505 | 0.407 | 0.321 | 0.507 | 0.445 | 0.483 | |

OCR | 0.677 | 0.507 | 0.391 | 0.299 | 0.399 | 0.521 | 0.740 | 0.754 | 0.823 |

INC | 0.704 | 0.652 | 0.670 | 0.642 | 0.607 | 0.560 | 0.602 | 0.597 | 0.567 |

ISD | 0.659 | 0.545 | 0.626 | 0.600 | 0.679 | 0.757 | 0.746 | 0.742 | 0.733 |

IR | 0.658 | 0.570 | 0.541 | 0.478 | 0.566 | 0.707 | 0.673 | 0.663 | 0.650 |

Es | 0.216 | 0.368 | 0.298 | 0.565 | 0.330 | 0.301 | |||

LAI | 0.242 | 0.406 | 0.298 | 0.238 | 0.284 | ||||

Ntree | 0.237 | 0.327 | 0.455 | 0.320 | 0.532 | 0.427 | 0.491 | 0.629 | 0.663 |

GS | 0.578 | 0.461 | 0.440 | 0.774 | 0.595 | 0.608 | 0.580 | 0.791 | 0.733 |

BDI | 0.199 | 0.145 | |||||||

AlG | 0.323 | 0.528 | 0.451 | 0.356 | 0.489 | 0.680 | 0.648 | 0.584 | 0.617 |

min_gi | 0.160 | ||||||||

Water | 0.209 | 0.339 | 0.324 | 0.241 | 0.403 | 0.433 | 0.382 | ||

school | 0.265 | 0.274 | 0.291 | 0.261 | 0.583 | 0.568 | 0.538 | 0.563 | 0.561 |

min_med | 0.471 | 0.328 | 0.329 | 0.292 | 0.458 | 0.653 | 0.642 | 0.575 | 0.711 |

tra | 0.267 | 0.241 | 0.241 | 0.203 | |||||

ret | 0.365 | 0.211 | 0.163 | ||||||

rest_pkt | 0.367 | 0.344 | 0.407 | 0.326 | 0.417 | 0.531 | 0.654 | 0.748 | 0.803 |

KMO | 0.374 | 0.407 | 0.395 | 0.368 | 0.466 | 0.562 | 0.595 | 0.641 | 0.661 |

**Table 3.**PCA loadings with communality (h

^{2}), eigenvalue (EV), explained Variance (Var) and cumulative Variance (cum Var) after dimension reduction. Green highlight absolute charge > 0.5 & orange highlight absolute charge > 0.3.

Dimension | Dim.1 | Dim.2 | Dim.3 | h^{2} | |
---|---|---|---|---|---|

Indicator | |||||

SLRV | −0.6730 | −0.3993 | 0.4578 | 0.8220 | |

INH | −0.7719 | 0.3924 | 0.0403 | 0.7515 | |

OCR | −0.3679 | −0.8388 | 0.0022 | 0.8390 | |

INC | −0.4715 | −0.8631 | −0.1230 | 0.9824 | |

ISD | 0.4523 | 0.8346 | 0.1495 | 0.9235 | |

IR | −0.4588 | −0.8500 | −0.1482 | 0.9550 | |

Ntree | −0.4972 | 0.3709 | −0.6629 | 0.8242 | |

GS | 0.7615 | −0.4143 | −0.0620 | 0.7554 | |

AlG | −0.6744 | 0.3258 | 0.2712 | 0.6345 | |

school | 0.5361 | −0.3397 | 0.4851 | 0.6380 | |

min_med | 0.7405 | −0.3764 | −0.1894 | 0.7258 | |

rest_pkt | 0.7248 | −0.3704 | −0.1007 | 0.6727 | |

EV | 4.46 | 3.99 | 1.07 | ||

Var | 37.20% | 33.26% | 8.91% | ||

cum Var | 37.20% | 70.46% | 79.37% |

**Table 4.**PCA loadings with communality (h

^{2}), eigenvalue (EV), explained Variance (Var) and cumulative Variance (cum Var). Green highlight absolute charge > 0.5 & orange highlight absolute charge > 0.3.

Dimension | Dim.1 | Dim.2 | Dim.3 | Dim.4 | Dim.5 | Dim.6 | Dim.7 | Dim.8 | Dim.9 | Dim.10 | Dim.11 | Dim.12 | h^{2} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Indicator | ||||||||||||||

SLRV | −0.6730 | −0.3993 | 0.4578 | −0.0058 | −0.2939 | 0.0080 | 0.0352 | −0.2983 | 0.0246 | 0.0036 | −0.0211 | 0.0157 | 1.0000 | |

INH | −0.7719 | 0.3924 | 0.0403 | 0.3873 | 0.1722 | −0.1718 | −0.0620 | −0.0528 | −0.0481 | 0.1069 | 0.1378 | −0.0059 | 1.0000 | |

OCR | −0.3679 | −0.8388 | 0.0022 | −0.0732 | 0.2674 | −0.0219 | −0.1471 | −0.0268 | −0.2309 | 0.0186 | −0.0875 | 0.0007 | 1.0000 | |

INC | −0.4715 | −0.8631 | −0.1230 | 0.0240 | −0.0489 | 0.0097 | 0.0439 | −0.0024 | 0.0570 | −0.0546 | 0.0299 | −0.0742 | 1.0000 | |

ISD | 0.4523 | 0.8346 | 0.1495 | −0.0305 | 0.0161 | −0.0024 | −0.0448 | −0.1286 | −0.1457 | −0.1801 | 0.0520 | −0.0198 | 1.0000 | |

IR | −0.4588 | −0.8500 | −0.1482 | 0.0245 | −0.0079 | 0.0064 | 0.0303 | 0.0911 | −0.0039 | −0.1435 | 0.1113 | 0.0455 | 1.0000 | |

Es | −0.4972 | 0.3709 | −0.6629 | 0.1879 | −0.2411 | 0.1831 | 0.1748 | −0.0631 | −0.1144 | 0.0191 | −0.0278 | 0.0030 | 1.0000 | |

LAI | 0.7615 | −0.4143 | −0.0620 | −0.3806 | −0.0274 | 0.2441 | −0.0327 | −0.0894 | −0.0535 | 0.1070 | 0.1266 | −0.0026 | 1.0000 | |

Ntree | −0.6744 | 0.3258 | 0.2712 | −0.4759 | −0.0041 | −0.0989 | 0.3238 | 0.1273 | −0.0842 | 0.0253 | 0.0186 | −0.0055 | 1.0000 | |

GI | 0.5361 | −0.3397 | 0.4851 | 0.5132 | −0.0539 | 0.2036 | 0.1782 | 0.1240 | −0.0828 | 0.0154 | 0.0016 | −0.0043 | 1.0000 | |

AlG | 0.7405 | −0.3764 | −0.1894 | 0.0901 | 0.3126 | −0.1763 | 0.3209 | −0.1821 | 0.0318 | −0.0054 | −0.0086 | 0.0064 | 1.0000 | |

school | 0.7248 | −0.3704 | −0.1007 | 0.0345 | −0.4206 | −0.3681 | −0.0364 | 0.0530 | −0.0917 | 0.0311 | 0.0130 | −0.0022 | 1.0000 | |

EV | 4.46 | 3.99 | 1.07 | 0.84 | 0.53 | 0.34 | 0.30 | 0.20 | 0.12 | 0.08 | 0.06 | 0.01 | ||

Var | 37.20% | 33.26% | 8.91% | 6.97% | 4.39% | 2.84% | 2.53% | 1.64% | 1.00% | 0.68% | 0.50% | 0.07% | ||

cum Var | 37.20% | 70.46% | 79.37% | 86.34% | 90.73% | 93.57% | 96.11% | 97.75% | 98.75% | 99.43% | 99.93% | 100.00% |

**Table 5.**Results for multiple regression Model 1 (SLRV model basic; Equation (24)) r = 0.9192, adj. r

^{2}= 0.8298, se = 0.4043.

Variable | Coefficienten | Standard Error | t-Statistic | p-Value | Lower 95% | Upper 95% |
---|---|---|---|---|---|---|

INH | 0.2077 | 0.0506 | 4.1019 | 0.0001 | 0.1077 | 0.3078 |

INC | 2.5286 | 0.1950 | 12.9670 | 0.0000 | 2.1432 | 2.9139 |

ISD | 0.6203 | 0.1054 | 5.8850 | 0.0000 | 0.4120 | 0.8286 |

IR | −1.3472 | 0.1566 | −8.6040 | 0.0000 | −1.6566 | −1.0377 |

Ntree | −0.1871 | 0.0430 | −4.3529 | 0.0000 | −0.2721 | −0.1022 |

AlG | 0.3255 | 0.0485 | 6.7130 | 0.0000 | 0.2297 | 0.4213 |

min_med | −0.3928 | 0.0482 | −8.1507 | 0.0000 | −0.4881 | −0.2976 |

rest_pkt | 0.1095 | 0.0511 | 2.1439 | 0.0337 | 0.0086 | 0.2104 |

school | 0.2610 | 0.0442 | 5.9002 | 0.0000 | 0.1736 | 0.3484 |

**Table 6.**Results for multiple regression Model 2 Equation (25) r = 0.9192, adj. r

^{2}= 0.8440, se = EUR 30.31 per m

^{2}.

Variable | Coefficienten | Standard Error | t-Statistic | p-Value | Lower 95% | Upper 95% |
---|---|---|---|---|---|---|

b | 90.9930 | 3.5926 | 25.3282 | 0.0000 | 83.8960 | 98.0901 |

m | 0.4575 | 0.0158 | 28.9734 | 0.0000 | 0.4263 | 0.4887 |

**Table 7.**Results for multiple regression Model 3 Equation (26) r = 0.9504, adj. r

^{2}= 0.9019, se = EUR 24.03 per m

^{2}.

Variable | Coefficienten | Standard Error | t-Statistic | p-Value | Lower 95% | Upper 95% |
---|---|---|---|---|---|---|

b | 103.7918 | 3.1455 | 32.9971 | 0.0000 | 97.5776 | 110.0060 |

m | 0.1844 | 0.0311 | 5.9293 | 0.0000 | 0.1230 | 0.2459 |

n | 0.0006 | 0.0001 | 9.5908 | 0.0000 | 0.0005 | 0.0008 |

**Table 8.**Basic data for the scenario development and model interpretation based on the “Zero Model”.

SLRV (Model 3) EUR per m ^{2} | INH Items per ha | School m | INC EUR per a | ISD % per a | IR % | Ntree Items | AlG m | min_med m | rest_pkt m | |
---|---|---|---|---|---|---|---|---|---|---|

Mean | 152.67 | 77 | 504 | 866,933,142,769 | 1.3 | 10.6 | 231 | 421 | 212 | 237 |

Z | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

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**MDPI and ACS Style**

Fauk, T.; Schneider, P.
Does Urban Green Infrastructure Increase the Property Value? The Example of Magdeburg, Germany. *Land* **2023**, *12*, 1725.
https://doi.org/10.3390/land12091725

**AMA Style**

Fauk T, Schneider P.
Does Urban Green Infrastructure Increase the Property Value? The Example of Magdeburg, Germany. *Land*. 2023; 12(9):1725.
https://doi.org/10.3390/land12091725

**Chicago/Turabian Style**

Fauk, Tino, and Petra Schneider.
2023. "Does Urban Green Infrastructure Increase the Property Value? The Example of Magdeburg, Germany" *Land* 12, no. 9: 1725.
https://doi.org/10.3390/land12091725