Green-Buildings.Rmd

---
title: "Green_Buildings"
author: "India Lindsay"
date: "8/31/2020"
output:
  md_document:
    variant: markdown_github
---

## The Case  

Over the past decade, both investors and the general public have paid increasingly close attention to the benefits of environmentally conscious buildings. There are both ethical and economic forces at work here.  In commercial real estate, issues of eco-friendliness are intimately tied up with ordinary decisions about how to allocate capital. In this context, the decision to invest in eco-friendly buildings could pay off in at least four ways.  

1. Every building has the obvious list of recurring costs: water, climate control, lighting, waste disposal, and so forth. Almost by definition, these costs are lower in green buildings.  
2. Green buildings are often associated with better indoor environments, the kind that are full of sunlight, natural materials, and various other humane touches. Such environments, in turn, might result in higher employee productivity and lower absenteeism, and might therefore be more coveted by potential tenants. The financial impact of this factor, however, is rather hard to quantify ex ante; you cannot simply ask an engineer in the same way that you could ask a question such as, How much are these solar panels likely to save on the power bill?
3. Green buildings make for good PR. They send a signal about social responsibility and ecological awareness, and might therefore command a premium from potential tenants who want their customers to associate them with these values. It is widely believed that a good corporate image may enable a firm to charge premium prices, to hire better talent, and to attract socially conscious investors.  
4. Finally, sustainable buildings might have longer economically valuable lives. For one thing, they are expected to last longer, in a direct physical sense. (One of the core concepts of the green-building movement is life-cycle analysis, which accounts for the high front-end environmental impact of acquiring materials and constructing a new building in the first place.) Moreover, green buildings may also be less susceptible to market risk in particular, the risk that energy prices will spike, driving away tenants into the arms of bolder, greener investors.  

Of course, much of this is mere conjecture. At the end of the day, tenants may or may not be willing to pay a premium for rental space in green buildings. We can only find out by carefully examining data on the commercial real-estate market.  

The file [greenbuildings.csv](../data/greenbuildings.csv) contains data on 7,894 commercial rental properties from across the United States. Of these, 685 properties have been awarded either LEED or EnergyStar certification as a green building. You can easily find out more about these rating systems on the web, e.g. at www.usgbc.org. The basic idea is that a commercial property can receive a green certification if its energy efficiency, carbon footprint, site selection, and building materials meet certain environmental benchmarks, as certified by outside engineers.

A group of real estate economists constructed the data in the following way.  Of the 1,360 green-certified buildings listed as of December 2007 on the LEED or EnergyStar websites, current information about building characteristics and monthly rents were available for 685 of them.  In order to provide a control population, each of these 685 buildings was matched to a cluster of nearby commercial buildings in the CoStar database.  Each small cluster contains one green-certified building, and all non-rated buildings within a quarter-mile radius of the certified building.  On average, each of the 685 clusters contains roughly 12 buildings, for a total of 7,894 data points.

The columns of the data set are coded as follows:

- CS.PropertyID:  the building's unique identifier in the CoStar database.  
- cluster:  an identifier for the building cluster, with each cluster containing one green-certified building and at least one other non-green-certified building within a quarter-mile radius of the cluster center.  
- size:  the total square footage of available rental space in the building.  
- empl.gr:  the year-on-year growth rate in employment in the building's geographic region.  
- Rent:  the rent charged to tenants in the building, in dollars per square foot per calendar year.  
- leasing.rate:  a measure of occupancy; the fraction of the building's available space currently under lease.  
- stories:  the height of the building in stories.  
- age:  the age of the building in years.  
- renovated:  whether the building has undergone substantial renovations during its lifetime.  
- class.a, class.b:  indicators for two classes of building quality (the third is Class C).  These are relative classifications within a specific market.  Class A buildings are generally the highest-quality properties in a given market.  Class B buildings are a notch down, but still of reasonable quality.  Class C buildings are the least desirable properties in a given market.  
- green.rating:  an indicator for whether the building is either LEED- or EnergyStar-certified.  
- LEED, Energystar:  indicators for the two specific kinds of green certifications.  
- net:  an indicator as to whether the rent is quoted on a "net contract" basis.  Tenants with net-rental contracts pay their own utility costs, which are otherwise included in the quoted rental price.  
- amenities:  an indicator of whether at least one of the following amenities is available on-site: bank, convenience store, dry cleaner, restaurant, retail shops, fitness center.  
- cd.total.07:  number of cooling degree days in the building's region in 2007.  A degree day is a measure of demand for energy; higher values mean greater demand.  Cooling degree days are measured relative to a baseline outdoor temperature, below which a building needs no cooling.  
- hd.total07:  number of heating degree days in the building's region in 2007.  Heating degree days are also measured relative to a baseline outdoor temperature, above which a building needs no heating.  
- total.dd.07:  the total number of degree days (either heating or cooling) in the building's region in 2007.  
- Precipitation:  annual precipitation in inches in the building's geographic region.
- Gas.Costs:  a measure of how much natural gas costs in the building's geographic region.  
- Electricity.Costs:  a measure of how much electricity costs in the building's geographic region.  
- cluster.rent:  a measure of average rent per square-foot per calendar year in the building's local market.  


### The goal

An Austin real-estate developer is interested in the possible economic impact of "going green" in her latest project: a new 15-story mixed-use building on East Cesar Chavez, just across I-35 from downtown.  Will investing in a green building be worth it, from an economic perspective?  The baseline construction costs are $100 million, with a 5% expected premium for green certification.

The developer has had someone on her staff, who's been described to her as a "total Excel guru from his undergrad statistics course," run some numbers on this data set and make a preliminary recommendation.  Here's how this person described his process.

>I began by cleaning the data a little bit.  In particular, I noticed that a handful of the buildings in the data set had very low occupancy rates (less than 10\% of available space occupied).  I decided to remove these buildings from consideration, on the theory that these buildings might have something weird going on with them, and could potentially distort the analysis.  Once I scrubbed these low-occupancy buildings from the data set, I looked at the green buildings and non-green buildings separately.  The median market rent in the non-green buildings was $25 per square foot per year, while the median market rent in the green buildings was $27.60 per square foot per year: about $2.60 more per square foot.  (I used the median rather than the mean, because there were still some outliers in the data, and the median is a lot more robust to outliers.)  Because our building would be 250,000 square feet, this would translate into an additional $250000 x 2.6 = $650000 of extra revenue per year if we build the green building.

>Our expected baseline construction costs are $100 million, with a 5% expected premium for green certification.  Thus we should expect to spend an extra $5 million on the green building.  Based on the extra revenue we would make, we would recuperate these costs in $5000000/650000 = 7.7 years.  Even if our occupancy rate were only 90%, we would still recuperate the costs in a little over 8 years.  Thus from year 9 onwards, we would be making an extra $650,000 per year in profit.  Since the building will be earning rents for 30 years or more, it seems like a good financial move to build the green building.


The developer listened to this recommendation, understood the analysis, and still felt unconvinced.  She has therefore asked you to revisit the report, so that she can get a second opinion.

```{r setup, include=FALSE, warnings=FALSE}
knitr::opts_chunk$set(echo = TRUE)
#loading required packages
library(ggplot2)
library(gridExtra)

```

# Case Analysis: Is A Green Building Worth It?

```{r, include=FALSE, warnings=FALSE}
Green = read.csv('C:/Users/india/Documents/GitHub/STA380/data/greenbuildings.csv')

#Count of the number of buildings with occupancy rate below 10%
green2 = dim(Green[Green$leasing_rate <= 10,])[1]
Green = Green[Green$leasing_rate > 10,] #and dropping them 


greenyes = Green[Green$green_rating==1,] #green buildings
greenno = Green[Green$green_rating==0,] #non green buildings
nyes = dim(greenyes)[1] #number of green buildings
nno = dim(greenno[1]) #number of non green buildings
```

  The Austin-based real estate developer is seeking to understand the financial returns to green certification for her new building. Construction of the building is priced at $100 million, with an expected $5 million premium for green certification. An analyst on the developer's staff estimated that green certification for the building will result in an additional $650,000 of yearly revenue and that it will take 7.7 years to recuperate the premium, considering the building has an occupancy rate of 100%. The analyst calculated his estimates by looking at all buildings with leasing rates greater than 10%. The goal of our analysis is to revisit the analyst's estimates, using the same data set containing statistics on 7,894 buildings, and determine if the presented returns to green certification are accurate.
  
  
### Newer, Taller, Larger Buildings Have Higher Rental Rates

  The developer's building is 15 stories tall, 250,000 square feet, and has an age of 0. To evaluate if the analyst should have accounted for our building's physical features, the below plots reveal the general relationships between Rent and each of these features. The gray shading around the line represents our margin of error for this estimate and the vertical red line represents our building.
  

```{r, echo=FALSE, warnings=FALSE, fig.height = 4, fig.width = 8, fig.align='center'}
ages = ggplot(Green, aes(x=age, y=Rent)) + geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE) +
  geom_vline(xintercept=0,linetype="dashed", color = "red") +
  labs(title="Age vs Rent",
    subtitle = "Newer Buildings Rent For More",
    x = "Age of Buildings", y="Yearly Rent Per Square Foot"
  )

#geom_point(aes(age,Rent,color=green_rating))

stories = ggplot(Green, aes(stories, Rent)) + geom_vline(xintercept=15,linetype="dashed", color = "red") +
  geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE) +
  labs(
    title = "Stories vs Rent",
    subtitle = "Taller Buildings Rent For More", caption="(for buildings less than ~75 stories)",
    x = "Number of Stories", y="Yearly Rent Per Square Foot"
  )

size = ggplot(Green, aes(size, Rent)) + geom_vline(xintercept=250000,linetype="dashed", color = "red") +
  geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE) +
  labs(title='Size vs Rent',
    subtitle = "Larger Buildings Rent For More",
    x = "Building Size", y="Yearly Rent Per Square Foot"
  )

grid.arrange(ages,stories,size,ncol=3)
```
 
### Location, Location, and... Location

 The building is located on East Cesar Chavez in Austin, Texas. Using outside sources, we were able to identify the local employment growth rate (2.5% as of March 31, 2020: [source](https://www.austinchamber.com/blog/04-21-2020-job-growth-unemployment)), average annual rainfall (34 inches: [source](https://www.weather.gov/media/ewx/climate/ClimateSummary-ewx-Austin.pdf)), and the number of degree days (2,248 heating days, 2,862 cooling days, and 5,110 total degree days: [source](https://www.eia.gov/energyexplained/units-and-calculators/degree-days.php)). The data set measured the values for degree days in 2007 however, as the 2007 estimates were unavailable, we used the 2018 values as they are likely a close estimate. We were unable to find information on the other location-based variables present in the data set.
 
  The below plots investigate whether these geographical characteristics have any affect on a building's rent. The red dashed line represents the data set's mean rent. While the direct relationship between the following location-based features and rent may not be clear, they help us understand where the rental market is inflated. 
 
```{r, echo=FALSE, warnings=FALSE, fig.height = 4, fig.width = 8, fig.align='center'}

P <- ggplot(Green, aes(x=Rent,Precipitation)) + geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE) + 
  geom_hline(yintercept=mean(Green$Rent), linetype="dashed", color = "red") +
  labs(
    title = "Precipitation vs Rent", subtitle="Wetter Cities Have Higher Rent",
    x = "Annual Rainfall (inches)", y="Yearly Rent Per Square Foot"
  )

E <- ggplot(Green, aes(x=empl_gr,Rent)) + geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE) + geom_hline(yintercept=mean(Green$Rent), linetype="dashed", color = "red") +
  labs(
    title = "Employment Growth Rate vs Rent", subtitle="Negative Rates: Rent Fluctuates, Positive Rates: Rent More Stable",
    x = "Employment Rate", y="Yearly Rent Per Square Foot"
  ) + theme(plot.subtitle=element_text(size=8))

grid.arrange(E,P, ncol=2)

```

The left plot indicates how rent is only stable for regions with positive employment growth rates. Otherwise, rent greatly varies for different levels of employment growth rates. The right plot reveals how rent spikes for regions with greater precipitation. This graph may be capturing the high rental rates for cities that receive high rainfall, such as San Francisco and the Pacific Northwest region. 


```{r, echo=FALSE, warnings=FALSE, fig.height = 4, fig.width = 8, fig.align='center'}

D <- ggplot(Green, aes(x=total_dd_07,Rent)) + geom_smooth(formula = y ~ s(x, bs = "cs"), method='gam', se = TRUE)  + geom_hline(yintercept=mean(Green$Rent), linetype="dashed", color = "red") +
  labs(
    title = "Total Degree Days vs Rent", caption = "Total Degree Days Accounts For Both Cooling And Heating Days",
    x = "Total Degree Days", y="Yearly Rent Per Square Foot"
  ) 
D


```
In the above plot, the big spike that occurs around 6,000 degree days is likely capturing cities with more extreme temperatures; NYC, Chicago, or Miami. The high left values with more moderate-climates may represent the Pacific Northwest region. The dip in the lower right might capture northern regions with very extreme weather and low property values. 

The relationship between rent and regional variables is complicated. Ultimately, it is best to account for as many physical and regional features as possible when estimating returns to green certification.


### Taking A Relevant Subset

  We obtained a subset of the buildings data set that contained buildings within +/- 1 standard deviations of our building's size, stories, age, regional employment growth rate, regional annual rainfall, and regional degree days. We dropped buildings that had less than 10% occupancy to avoid outliers. This data set contained information on 314 buildings, 73 of which were green and 241 of which were non-green.
  

```{r, include=FALSE, warnings=FALSE}
#getting SD of our variables
sd(Green$size) #297,533.4
sd(Green$stories) #12.287
sd(Green$age) #32
sd(Green$empl_gr) #NA? 
emp = na.omit(Green$empl_gr)
sd(emp) #8.163
sd(Green$hd_total07) #1976.937
sd(Green$cd_total_07) #1104.589
sd(Green$Precipitation) #11.575

#creating subset
new_green = subset(Green, subset=((size<550000) & (stories>3) & (stories<27) & (age<32) & (empl_gr<8) & (Precipitation>23) & (Precipitation < 45) & (hd_total07>200) & (hd_total07 < 5200) & (cd_total_07>1700) & (cd_total_07 < 3900)))

#distinguishing between green vs non-green
newgreenyes = new_green[new_green$green_rating==1,]
newgreenno = new_green[new_green$green_rating==0,]

summary(newgreenyes$Rent)
summary(newgreenno$Rent)

```
  
### What Can We Learn From Buildings With Similar Features? 

  Looking only at similar buildings, the median rent for a green building is $22 while the median rent for a non-green building is $20.25. 


```{r, echo=FALSE, warnings=FALSE, fig.height = 3, fig.width = 7, fig.align='center'}
n = dim(new_green)[1]
ggplot(new_green, aes(x=Rent,y=factor(green_rating))) + geom_boxplot() +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + scale_y_discrete(labels=c("0" = "Non-Green", "1" = "Green")) + 
  labs(title="Green Buildings Have a Higher Average Rent", subtitle="Offering an Avg $1.75 More Per Sq. Ft.", y="Type of Building", x="Yearly Rent Per Square Foot")

```

  Green buildings also have a higher median leasing rate of 91.43% compared with the non-green leasing rate of 88.49%. 

```{r, echo=FALSE, warnings=FALSE, fig.height = 3, fig.width = 7, fig.align='center'}
#density plot for leasing rate
ggplot(data=new_green, aes(x=leasing_rate)) + geom_density(aes(fill=factor(green_rating))) + scale_fill_discrete(name = "Type of Building", labels = c("Non-Green","Green")) +
   labs(title="Green Buildings Tend to Have Higher Leasing Rates", x="Leasing Rate", y="Proportion of Buildings") 
```
 
### Other Lurking Features Remain 

The building's quality is measured in the data set by standards of high (class a), average (class b), or low (class c). The below plots reveal how the quality of the building has a significant association with higher rent per sq foot for buildings similar to ours. One would expect green certified buildings to be of higher quality however, these variables have a fairly low correlation of 0.248. 

```{r, echo=FALSE, warnings=FALSE, fig.height = 3, fig.width = 7, fig.align='center'}
#G_amenities = ggplot(newgreenyes, aes(x=Rent,y=factor(amenities))) + geom_boxplot() + scale_y_discrete(labels=c("0" = "Low Standard", "1" = "High Standard")) +  labs(title="Rent for Green Buildings By Standard of Amenities", subtitle="Avg Rent Per Sq Ft is $0.65 Greater for High Standard of Amenities", x="Leasing Rate", y="Standard of Amenities") 

#N_amenities = ggplot(newgreenno, aes(x=Rent,y=factor(amenities))) + geom_boxplot() + scale_y_discrete(labels=c("0" = "Low Standard", "1" = "High Standard")) +  labs(title="Rent for Non Green Buildings By Standard of Amenities", x="Leasing Rate", y="Standard of Amenities") 

#nice_amen = subset(newgreenyes, subset=((amenities==1)))
#median(nice_amen$Rent) #avg rent is 22

#not_amen = subset(newgreenyes, subset=((amenities==0)))
#median(not_amen$Rent) #avg rent is 21.69

#grid.arrange(G_amenities,N_amenities ,ncol=2)

G_class = ggplot(newgreenyes, aes(x=Rent,y=factor(class_a))) + geom_boxplot()  + scale_y_discrete(labels=c("0" = "Average or Low Quality", "1" = "Highest Quality")) +  labs(title="Rent for Green Buildings By Building Quality", subtitle="Avg Rent Per Sq Ft is $4 Greater for High Building Quality", x="Leasing Rate", y="Building Quality")

nice_class = subset(newgreenyes, subset=((class_a==1)))
avg_nice = median(nice_class$Rent) #avg rent is 23

low_class = subset(newgreenyes, subset=((class_a==0)))
avg_low = median(low_class$Rent) #avg rent is 19

N_class = ggplot(newgreenno, aes(x=Rent,y=factor(class_a))) + geom_boxplot()  + scale_y_discrete(labels=c("0" = "Average or Low Quality", "1" = "Highest Quality")) +  labs(title="Rent for Non-Green Buildings By Building Quality", subtitle="Avg Rent Per Sq Ft is $3 Greater for High Building Quality", x="Leasing Rate", y="Building Quality")

Nnice_class = subset(newgreenno, subset=((class_a==1)))
Navg_nice = median(Nnice_class$Rent) #avg rent is 23

Nlow_class = subset(newgreenno, subset=((class_a==0)))
Navg_low = median(Nlow_class$Rent) #avg rent is 19

G_class
N_class

cor_stat = cor(Green$green_rating,Green$class_a)
```
 

### Concluding Thoughts 

  The analyst's conclusion that a green certification yields a greater return was accurate however, his analysis was quite faulty. He greatly overestimated the degree of the return in yearly rent per square foot as he did not account for the building's characteristics. The analyst focused on the number of years it would take to recuperate the initial premium. As it is almost impossible that every individual square foot of a building could be rented out, these estimates are irrelevant. 
  
  Our analysis concluded that green certification is worth the extra premium. Green buildings offer an additional $1.75 in yearly rent per square foot, on average. Based upon higher average occupancy rates, it is more likely that green buildings will have a higher total level of income than non-green buildings. However, it is essential that the developer ensure her building is of high quality as this variable leads to a greater difference in rent for buildings similar to ours.