Craigslist Cham
New Insights into Rental Housing Markets Across the United …
Current sources of data on rental housing—such as the census or commercial databases that focus on large apartment complexes—do not reflect recent market activity or the full scope of the US rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized eleven million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the United States. We find that some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census gures – uploaded by Paul WaddellAuthor contentAll figure content in this area was uploaded by Paul WaddellContent may be subject to copyright. Discover the world’s research20+ million members135+ million publications700k+ research projectsJoin for free
New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings Authors: Geoff Boeing and Paul Waddell Department of City and Regional Planning, University of California, Berkeley Contact: Abstract: Current sources of data on rental housing – such as the census or commercial databases that focus on large apartment complexes – do not reflect recent market activity or the full scope of the U. S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data. Cite as: Boeing, G. and Waddell, P. 2016. “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings. ” Journal of Planning Education and Research, forthcoming. This is a pre-print of a forthcoming peer-reviewed article in the Journal of Planning Education and Research. See for the final version and citation information.
Boeing and Waddell 2 Introduction It would be difficult to overstate the importance of the rental housing market in the United States, despite longstanding cultural attitudes and policy frameworks encouraging homeownership (Schwartz 2010; Belsky 2013). The share of U. households that rent their housing grew from 31% in 2004 to 35% in 2012, accounting for a total of 43 million households by 2013 (Joint Center for Housing Studies 2013). Yet a large portion of this rental market activity takes place between private parties leaving minimal and inconsistent data trails. Commercial data sources typically only cover large apartment complexes, and census rental data are limited by their inability to provide reliable current estimates at the local scale or information about unit characteristics. Today, much of the rental listing activity that once occurred in the classified section of local newspapers has moved online to web sites specializing in housing advertisements. The Craigslist web site has become the dominant information exchange in this market and its users generate millions of rental listings each month, yet minimal research has been done to date to explore and understand the rental housing market represented by Craigslist. To address this knowledge gap in rental housing markets, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist U. rental listings. The data reveal new insights into spatial patterns in metropolitan housing markets across the U. S., and provide much richer detail at much finer scales than other publicly available data sources. New York and San Francisco unsurprisingly have the first and third highest rent per square foot, and North Dakota comes in second, reflecting its recent oil industry boom and housing shortage. We assess affordability by calculating rent burdens and proportions of listings below the U. Department of Housing and Urban Development (HUD) fair market rents (FMRs) for 58 metropolitan areas. Although 37% of the Craigslist listings in these metropolitan areas are below the corresponding HUD FMR, surprisingly some metros like New York and Boston are only in the single-digit percentages. We discover that Craigslist median rents are reasonably comparable to HUD estimates on average, but crucially offer planners more up-to-date data, including unit characteristics, from neighborhood to national scales. The objectives and motivation for this study are twofold. The first is to present several trends in this underexplored dataset and their implications for the housing market. It is the most comprehensive dataset currently available to examine the U. rental housing market. The second is to share with housing scholars and practitioners a powerful emerging data science methodology for collecting and investigating urban data. These methods of data collection, cleaning, and analysis address a growing need for planners to embrace unconventional and emerging tools to explore the vast array of decentralized user-generated data now flowing through cities. The recent explosion in big data and data science has been centered in the fields of computer science, statistics, and physics (O’Neill and Schutt 2013). Planners must understand these tools to help ground – in urban theory and empirical research – the growing urban big data literature being generated in these other fields that have increasingly turned their attention to cities (cf. Bettencourt and West 2010; Bettencourt 2013; Pollock 2016). Yet most importantly, real-time Craigslist data in particular fill a pressing need for planners to measure local-scale rental markets – which evolve quicker than 5-year census rolling averages and data release delays – to understand local conditions, advocate for realistic FMRs, and proactively address emerging affordability challenges.
Boeing and Waddell 3 We begin by providing a brief background on the rental housing market, Craigslist’s growing role in it, and the pressing need to study cities through nontraditional sources of big data. Next we explain our methodology for collecting this unique dataset, cleaning it, validating it, and analyzing it. Then we present our findings and discuss the practical implications of these housing insights – and urban big data generally – for planners. We conclude with a discussion of the generalizability of our methodology, and the prospects and challenges of big data for planning practitioners and urban scholars. Rental housing markets and big data Despite the importance of the U. rental housing market – particularly in the face of a critical shortage of affordable housing in many cities (Garde 2015) – there are no comprehensive data sources capturing its full scope. Most data used by housing planners come from two sources. The first source is associations of apartment managers and brokers that focus on large apartment complexes, through companies such as CoreLogic, CoStar, Reis, and CBRE. These commercially-maintained data sources are valuable, but provide insufficient information about significant segments of the rental market including garage apartments, condominiums and houses for rent, small self-managed apartment buildings, and granny flats – what Wegmann and Chapple (2013, 35) call “an amateur-operated rental market with some informal characteristics. ” The second source is the Census Bureau’s American Community Survey (ACS), an invaluable resource for social scientists studying small-scale demographic variation. However, it represents a very small sample of households and can produce inaccurate data (Macdonald 2006; Spielman et al. 2014). While the annual metropolitan-scale ACS data are useful for broad snapshots of rents, the ACS provides tract-level data only as a 5-year rolling average. Planners thus struggle to acquire up-to-date rental data at the local scale. Further, the ACS rental data provide little information about units. For instance, the median rent for a tract does not reveal what a family of 4 needs to pay to rent a 3-bedroom unit. Practitioners and urban scholars who want to monitor and gain insights into trends across the full spectrum of this market have been unable to do so effectively using existing data sources (Wegmann and Chapple 2013). Housing rental data from Craigslist address the preceding challenges, yielding millions of observations at fine spatial and temporal scales. Until 10 years ago, available rental units were primarily listed in the classified section of local newspapers. Today they are primarily advertised on web sites like Craigslist, which has become the foremost venue for U. rental housing listings (Hau 2006). Craigslist was founded in San Francisco in 1995 by Craig Newmark as an online classified advertisements service (Craigslist 2015). Today, it is the 11th most visited web site in the U. (Alexa 2015) and holds a near monopoly in the online rental listings space (Brown 2014). Between 2000 and 2007, Craigslist took a $5 billion bite out of newspapers’ ad revenue (Seamans and Zhu 2014). Kroft and Pope (2014) found that Craigslist precipitated a 10% reduction in average metropolitan rental vacancy rates and increased market efficiency by lowering search costs, thus reducing the average time for units to lease by three weeks. Few researchers have studied Craigslist rental listings directly, but those who have usually do so in the context of landlord discrimination and the Fair Housing Act (e. g., Kurth 2007; Decker 2010; Oliveri 2010; Hanson and Hawley 2011). Mallach (2010), however, hand-tabulated 105 Craigslist listings to estimate
Boeing and Waddell 4 the median rent in Phoenix. Wegmann and Chapple (2013) used a small sample of 338 Craigslist listings to study the prevalence of secondary dwelling units in the San Francisco Bay Area. Finally, Feng (2014) web-scraped 6, 000 Craigslist listings to study Seattle’s housing market. These listings are a type of Volunteered Geographic Information (VGI), defined as content that is both user-generated and geolocated. VGI is one of the most important and fastest-growing sources of geospatial big data (Jiang and Thill 2015). “Big data, ” though it varies in interpretation, is more than just a buzzword – it is a type of data that is meaningfully different from traditional and necessarily smaller-scale data (Mayer-Schönberger and Cukier 2013; Kitchin and McArdle 2016). Laney (2001) provides the classic definition of big data, characterized by the 3 Vs: volume, variety, and velocity. A 4th V, veracity, is sometimes added to signify volume’s ability to overcome traditional challenges with messiness and quality. As Jiang and Thill (2015, 1) describe it: “Small data are mainly sampled (e. g., census or statistical data), while big data are automatically harvested… from a large population of users. ” These massive datasets can represent very large samples at incredibly fine spatial and temporal scales, and have significant implications for urban planning and research. The “smart cities” paradigm promotes harnessing big data for richer understanding, prediction, and planning of cities, though not without controversy (Townsend 2013; Ching and Ferreira 2015; Goodspeed 2015). Big data are starting to have a paradigm-shifting impact on social science research, and data from Internet-based interactions have the potential to reshape our understanding of collective human dynamics (Watts 2007; Batty et al. 2012). Housing markets are ripe for such exploration. Rae (2015) recently looked at 800, 000 user-generated housing searches on the British site Rightmove to study the geography of submarkets. However, to date there has been minimal research on large-scale VGI rental listings or the substantial housing market represented by Craigslist. Methodology To narrow this knowledge gap and better understand this market, we collected 11 million rental listings from Craigslist across the U. between May and July 2014. We developed tools to clean the data, extract useful elements, organize them, and analyze them to investigate spatial and temporal patterns – including affordability – in the rental housing market. Throughout, it is important to remember that Craigslist listings provide advertised rents – not final negotiated rents in legal contracts. Metropolitan markets, neighborhoods, and individuals vary in levels of Internet access or technical savvy to list and search for housing online as a function of wealth, race, employment, education, language, social ties, rurality, and other sociodemographic traits (Mossberger et al. Some rental markets, such as New York’s, are dominated by brokers (Gordon 2006). Planners must consider these critical issues in any application of big data or VGI. Nevertheless, Craigslist presents an invaluable data source for housing research. Web scraping Data are usually transferred over the Internet by means of some formal, structured dataset easily processed by a computer. However, the Internet is awash in unstructured and semi-structured data never made available as a formal dataset: many web pages contain text content that is human-readable but not easily machine-readable. Web scraping bridges this gap and opens
Boeing and Waddell 5 up a new world of data to researchers by automatically extracting structured datasets from human-readable content (Mitchell 2015). A web scraper accesses web pages, finds specified data elements on the page, extracts them, transforms them if necessary, and finally saves these data as a structured dataset. This process essentially mimics how a web browser operates by accessing web pages and saving them to a computer’s hard drive cache. In our case, we simply use the contents of this cache for our subsequent analysis after cleaning and organizing the extracted data. A web scraper automates the otherwise cumbersome process of manually collecting data from many web pages and assembling structured datasets out of messy, unstructured text strewn across thousands or even millions of individual pages. Discussions of web scraping often raise questions of legality and fair use. There are three relevant considerations here: copyright, trespassing, and archives. First, a federal district court decided that it is not a violation of copyright to scrape publicly available data such as Craigslist listings (Craigslist Inc. v. 3Taps Inc. 2013). Moreover, research is a noncommercial fair use that neither repackages nor relists the data. Second, Craigslist has previously sued a company – 3Taps Inc., who scraped their data for competitive commercial purposes – but only after first sending them a cease-and-desist letter and blocking their IP addresses (ibid. ; Splichal 2015). The judge ruled that 3Taps, in effect, trespassed on Craigslist’s servers specifically by ignoring the cease-and-desist and using a proxy to circumvent the IP address restrictions that plainly forbid them from accessing the servers (Goldman 2013; Wolfe 2015). Terms of use are subject to change and should be consulted before proceeding on any such project. Third, other organizations such as the Internet Archive () scrape and snapshot Craigslist’s web pages along with millions of other web sites. Researchers can collect rental listings from these snapshots instead of from Craigslist directly, though they may be less detailed. We built a web scraper to collect rental listings from the Craigslist web site, using the Python programming language and the scrapy web scraping framework (Scrapy Community 2015). First, our web scraper visits a publicly available Craigslist web page that contains rental listings. Next, it receives HTML data back from the web server. This HTML defines the content of web pages (Reid 2015). Then, the scraper extracts the useful data elements from the HTML using the XPath query language (Kay 2008). Finally, our scraper saves these data to a structured dataset on a hard drive. We created a process to run the web scraper once each night, configured to collect every Craigslist rental listing that had been posted during the previous day and was still online. During our data gathering, we collected 11 million Craigslist rental listings across 415 regions (i. e., Craigslist’s geographic subdomains). This dataset covers every rental listing in every Craigslist U. subdomain between mid-May and mid-July 2014 (if a listing was posted and taken down on the same day, our scraper did not collect it). In this study we use the term region to refer to these Craigslist subdomains, which can correspond to metropolitan areas, counties, or states depending on the region in question. Craigslist geographies are not always a perfect match for census geographies (e. g., a unit in southern New Hampshire might be listed in either Craigslist’s New Hampshire or Boston regions), but the vast majority of listings are far from these gray-area boundaries and the geographies do generally correspond well. For comparability, we used census Combined Statistical Areas when they better matched the Craigslist geography (e. g., in the San Francisco Bay Area) or conflated Craigslist regions to better match Metropolitan Statistical Areas (MSAs)
Boeing and Waddell 6 (e. g., combining Craigslist’s Los Angeles and Orange County regions to match the corresponding census MSA). Data cleaning As is common when collecting VGI, our raw data were very messy. Individual people created these listings through generally free-form text entry, so the rental data we retrieved from Craigslist required substantial filtering and cleaning. We will henceforth refer to the initial, complete, and uncleaned dataset as the original dataset. Descriptive statistics for these datasets and processing steps are summarized in Table 1 and more details are in the Appendix. Table 1. Descriptive statistics for the dataset at successive stages of processing Note: The original dataset contains the complete original set of listings. The unique dataset retains one listing per unique ID. The thorough dataset retains unique listings that contain rent and square foot data. The filtered dataset retains thorough listings with reasonable values for rent, square footage, and rent per square foot. The geolocated dataset retains listings from the filtered dataset that contain latitude and longitude. The mean rent per square foot and rent per square foot standard deviation drop sharply between the thorough and filtered datasets, while robust statistics – such as the interquartile range (IQR) and medians – are virtually unchanged. The first step was the identification and flagging of duplicate listings. Craigslist allows users to resubmit a listing multiple times (retaining the same listing ID), after a couple of days’ interval, to restore it to the top of the search results and improve its visibility. Thus, we considered a listing to be a duplicate if its ID appeared more than once in the dataset. We will henceforth refer to the set of unique listings as the unique dataset. Next, we retained only those unique listings with rent and square footage data as the thorough dataset. Rent and square footage cannot contain negative values: the thorough dataset’s rent, square footage, and rent per square foot means are much greater than their medians since the distribution is strongly positively skewed by outliers, such as some rents in the billions of dollars. Such values are clearly typos (e. g., billion dollar rents), spam (e. g., $1 listings linking to an external web site), or other forms of garbage data (e. g., houses listed for sale in the rentals section). Accordingly, we filtered the thorough dataset to retain only those listings that had reasonable values for rent, square footage, and rent per square foot. Table 2. Data filtering values Min reasonable value (0. 2 percentile) Max reasonable value (99. 8 percentile)
Boeing and Waddell 7 To define a “reasonable” range, we took the values at the 0. 2 percentile and the 99. 8 percentile nationwide for each of these three fields as minima and maxima to minimize truncation and provide sensible ranges (Table 2). Thus, a reasonable value is one in the middle 99. 6% of each variable’s distribution. We used these percentiles (rather than the second or third standard deviations above/below the mean) because they provide more realistic value ranges and nationwide criteria give us clear comparability across metropolitan markets. $10, 000 rent or $12 per square foot is not unheard of in expensive markets like New York or San Francisco, and $189 rent or $0. 10 per square foot is plausible for certain properties in the least expensive markets. The range of reasonable square footage values also corresponds to a range from very small studios to large detached homes. We will refer to this set of listings filtered by reasonable values as the filtered dataset. Lastly, we retained only those rows with latitude and longitude data from the filtered dataset as the geolocated dataset. Listing creators (optionally) assign latitude and longitude by dropping a pin onto an OpenStreetMap interactive web map to explicitly indicate the location of the rental unit. This avoids many of the problems associated with geocoding addresses (e. g., Cayo and Talbot 2003; Zandberger 2008), yet accuracy depends on the user placing the pin in the correct location. Data analysis We analyzed the Craigslist data by region to assess several housing market characteristics, including distributions of rents, square footage, and rents per square foot. To investigate rental affordability patterns, we merged our dataset with HUD’s 2014 FMR estimates and the 2014 ACS 1-year estimates of median household income and resident population. We used all the listings in the filtered dataset for each region that either 1) corresponds to one of the 50 most populous MSAs or 2) is among the 50 regions with the most total listings posted. This sample comprises 58 metropolitan areas and 78% of the listings in our filtered dataset, and most importantly allows us to compare the Craigslist data to HUD data consistently and with appropriate spatial and population extents. FMRs are established for policy purposes and generally correspond to 40th percentile rents, “the dollar amount below which 40 percent of the standard-quality rental housing units are rented” (USHUD 2007, p. 1). FMR “areas” generally correspond to metropolitan areas but HUD uses a more complicated formula to determine percentiles and spatial boundaries in certain circumstances (ibid. ). For each Craigslist region in the sample, we calculated the rent proportion of income (i. e., the ratio of Craigslist median rent to median monthly household income) and an estimate of how many square feet can be rented in each region for the nationwide median rent (calculated by dividing nationwide median rent by regional median rent per square foot). Then we calculated the proportion of listings in the filtered dataset at or below the HUD FMR, per region and number of bedrooms. Finally, we mapped our geolocated dataset with a GIS to visualize spatial patterns within and between regions. Findings National spatial patterns Across the entire filtered dataset, the median rent is $1, 145, the median square footage is 982, the median rent per square foot is $1. 11, and both the mean and median number of
Boeing and Waddell 8 bedrooms are approximately 2. The map in Figure 1 depicts 1. 5 million rental listings in the contiguous U. in our geolocated dataset. Rents per square foot are represented in nationwide quintiles. This map reveals spatial patterns that generally conform to our expectations for the U. housing market: large cities on both coasts have higher rents. The map clearly depicts large swaths of high rents per square foot throughout the Boston-Washington corridor and along the coast of California. Other smaller hotspots exist along the coast of southern Florida and in the metropolitan areas of large, affluent cities like Chicago, Denver, and Seattle. Figure 1. 1. in the geolocated dataset, divided into quintiles by each listing’s rent per square foot. The interior areas of the U. have a sprinkling of less-expensive data points punctuated by middle-quintile clusters around major cities and regional centers. The small towns in the Rocky Mountains west of Denver appear as mini-clusters of expensive rents, due to the significant luxury housing markets in resort towns like Vail and Aspen (Dowall 1981; Lutz 2014). Rental listings in North Dakota generally have extremely high rents per square foot, reflecting recent oil income and unmet demand for housing in oil-producing areas (Holeywell 2011; Brown 2013). In fact, North Dakota’s listings have the second-highest median rent per square foot of any region in the entire dataset, as the timeframe of our data collection preceded the collapse of oil prices in 2015 which put substantial downward pressure on rents in North Dakota (Scheyder 2015). Overall, New York, North Dakota, San Francisco, Boston, and Santa Barbara are the most expensive regions (Figure 2). The other usual suspects from Southern California, Hawaii (cf. Boeing 2016), and the Eastern Seaboard also pepper this list. In contrast, the lowest-priced regions in the dataset are small towns across the country that happen to have their own Craigslist subdomains.
Boeing and Waddell 9 Figure 2. The most expensive (left) and the most populous (right) Craigslist regions in the filtered dataset, by median rent per square foot. The horizontal line depicts the nationwide median. Different regions have different statistical distributions of rent per square foot values. Most are heavily right-tailed, but this is a function of the region’s median value. We estimated the frequency of rent per square foot values for all 415 regions, each represented by its own line (Figure 3). These data show the heavy-tailed distributions that would be expected of diverse and heterogeneous spatial data (Anderson 2006). The regions with the most skewed distributions also indicate a more extreme gap between the highest end and the rest of the market. The gradient reveals the relationship between rent per square foot’s per-region median, mode, and statistical dispersion: regions with lower median rents per square foot tend to peak at lower values and tend to be more peaked. This compression of rents in soft markets is a significant finding for planners. In Detroit, most of the listed units are concentrated within a narrow band of rent per square foot values, but in San Francisco rents are much more dispersed. This suggests to practitioners and policymakers that FMR-based housing vouchers – designed to unlock neighborhoods of opportunity to the poor – may serve different functions in high-cost versus low-cost areas. The typical 40% FMR might be insufficient to upgrade neighborhoods in statistically dispersed markets like San Francisco, especially when considering household size. Figure 3. Probability densities of the rent per square foot values for each of the 415 Craigslist regions in the filtered dataset (left), with the 15 most populous broken out for detail (right). Each region has its own line, colored by median rent per square foot for that region. The area under the curve between any two points represents the probability for that interval.
Boeing and Waddell 10 Affordability “Rent burden” is typically defined by rent exceeding 30% of household income (Quigley and Raphael 2004; Schwartz 2010; Aratani et al. 2011). Although this flat ratio has been critiqued by some for oversimplifying affordability, it remains the standard convention in research and practice (USHUD 2014). We calculated the “rent proportion” – what share of its income a typical household would spend on a typical Craigslist rent – for each metropolitan area. At their median values, New York, Los Angeles, San Francisco, Miami, Boston, and San Diego all exceed the rent burden threshold. The most populous metropolitan areas’ rent proportions demonstrate a wide variation in burden (Figure 4; details in Appendix). This is a useful indicator of affordability for local and regional planners. Figure 4. Left: ratio of median rent to median monthly household income for the 15 most populous metropolitan areas in the U. Right: proportion of listings in the filtered dataset at/below HUD FMR, per Craigslist region (excluding Dallas – see Appendix). The horizontal lines respectively represent the standard 30% definition of rent burden and the standard 40th percentile FMR. We also calculated the proportion of listings in the filtered dataset at or below the HUD FMR, per region and number of bedrooms. HUD FMR values generally define the 40th percentile rent identified by regional surveys that exclude certain public and subsidized housing, among other requirements. We would expect these proportions of listings to typically be about 0. 4, since the 40th percentile value is greater than 40% of all values. As previously discussed, the Craigslist data represent median advertised rents while the HUD data represent a sample of rents paid. Yet, in total, 37% of the listings in these regions are below the corresponding HUD FMR – quite close to the expected value of 40%. However there is considerable variation. While more than two-thirds of the listings in regions like Phoenix, Las Vegas, and Kansas City are below the FMR, New York and Boston have only single-digit percentages of listings below this threshold (Figure 4; details in Appendix). This is a troubling finding for planners. As discussed earlier, FMRs might be insufficient for households trying to upgrade neighborhoods in metros with highly dispersed rent values; they also appear to limit housing seekers in New York and Boston to very narrow slices of available housing units. The disconnect between current listings and FMR levels might be due to an interaction of factors: 1) the prevalence of rent control in certain markets; 2) FMR calculations lagging behind the market; and 3) FMR calculations’ basis on 5-year ACS estimates. For example, the HUD FMR for a two bedroom unit in Alameda County, California (in the San Francisco Bay Area)
Boeing and Waddell 11 dropped from $1, 585 in 2015 to $1, 580 in 2016 – despite the region’s skyrocketing rents since mid-2013 – because HUD extrapolated FMR from the 2013 5-year ACS estimate. HUD currently requires housing authorities to conduct their own (time consuming and expensive) survey of rents to protest the established FMR. Craigslist data offer an invaluable real-time alternative to easily take the pulse of local housing rental markets at fine scales to inform superior, more-current estimates – particularly when subjected to some ground-truthing or supplemented with a limited traditional survey. Figure 5. Rental power indicator: how many square feet one can rent in each of the 15 most populous metropolitan areas for the nationwide median rent of $1, 145, given each’s median rent per square foot. The horizontal line represents the nationwide median square footage in the filtered dataset. Intermetropolitan variation is ripe for future research, as it may offer nuance to HUD values or shed light on local market behaviors. Of the 15 most populous metropolitan areas in the U. S., the large cities in California and along the Eastern Seaboard have the highest median rents per square foot, while those of large cities elsewhere in the Sunbelt (plus Detroit) are much lower (Figure 2). New York’s median rent per square foot is more than 3. 5 times higher than Atlanta’s, reflecting underlying differences in land values which capitalize intermetropolitan variation in amenities, incomes, demand, and supply. Conversely, the “rental power” indicator represents an estimate of how many square feet can be rented in each region (given each’s median rent per square foot) for the nationwide median rent of $1, 145. Memphis offers the greatest value among these regions at 1, 659 square feet, while New York offers the least at 398 square feet (Figure 5; details in Appendix). This indicator facilitates nationwide comparisons but does not explicitly represent what a typical worker can afford in each region, as wages vary between them. Metropolitan spatial patterns Planning practitioners require current data in particular at the local scale. The Craigslist
Cham on Craigslist!! | Chameleon Forums
Cham on Craigslist!! | Chameleon Forums
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Thread starter
RSGriff
Start date
May 18, 2009
#1
Someone either help me save, or please save this cham!
craigslist entry:
“I have a 5 month year old baby chameleon. Its a female. These pets need alot of caring so if you dont have alot of free time you shouldn’t be interested. Thats why im trying to get rid of her. I just bought her on my birthday in November. I have the chameleon, 10 gallon tank, a little log thing to go under, a miniature tree, and a water bowl. For everything i paid a little over $250. Im trying to get $150 or best offer. ”
I was doing my rounds on craigslist looking for possible items for my cages when I came across this poor soul. Im thinking about trying to help since I could drive the 2. 5 hrs to pick it up, but there is no way I am paying more than $20 for a cham kept in these conditions and I have no use for the aquarium that she is kept in. Could someone help me phrase an email that explains why I wont pay much in a polite way.
#2
refer him to this website or the raising kitty website.
#3
How does this sound?
Hello,
I am interested in your female chameleon. It looks to be a veiled from the picture but I cannot tell. If it is, the conditions it is living in may not be correct for its local. You may want to check out for some tips on caring for them such as watering systems, proper lighting and cage types and decor. Because of these conditions, especially using a water bowl instead of a mist or drip system (and I don’t see a proper UVB bulb being used), I’m worried there may be some health issues which would reduce the amount I am willing to pay. I would like to help you by taking the chameleon off your hands, but like I said I would not be willing to pay much for the animal. My offer is $20 and I could pick up.
-Ryan
#4
Very nice. To the point and polite.
#5
I really think these threads should be kept off ChameleonForums, it causes too much debate etc.
I think what should be done is a reference to this site, a nice email, and not much more…
Most people are just too stubborn, especially if they are going to sell them anyways, they obviously want them gone!
Just my two cents.
Syn
#6
I sent an email saying about the same thing you did and recieved an insulting email in return about how they knew what they where doing or the cham wouldnt still be alive and how no where in the add did it say they where in need of correcting the way they cared for them cham. Kinda hurt my feelins and the person refused to sell me the poor cham without the poor caging included…..
might have better luck than I did.
#7
what on here doesn’t start a debate >. < there is at least one debate started every day. and everyone's guilty for starting at least one...
#8
I have not received one back yet... but we will see
#9
here are my thoughts on this....
DON'T DO IT, DO NOT BUY THIS CHAMELEON!!!!!
first off, this chameleon can not be 5 months old...
he has owned it for around 6 months. since Nov he has had it.
then, it had to be about 3 months old before a breeder would sell it to a petstore.
that makes it about 9 months old.
then, for at least 5 or 6 months it has not had a UVB bulb.
I smell MBD starting now or in the near he's been dusting with D3 almost every day... let's face it. with a glass tank, no UVB bulb, moss type substrate, "a little log thing to go under", a fake plant (not even big enough), AND A WATER BOWL (sigh).. chances are he has been using an "all-in-one" vitamin dust like you would find at a local petstore.
(at least in my local petco and petland, all they have is something like "T-REX" vitamin dust w/D3 in it, and that is it. nothing else. although they also have reptivite w/D3 as well. )
lastly, just look at the size of that poor 's smaller then my 3 month old male panther. AND IT'S ABOUT 9 MONTHS OLD!!!!!
why, why, why, would anyone want to buy such a chamelen that clearly has no potential to last more then 1 vet visit?
this poor chameleon will clearly die soon regardless that anyone here would do their best to make it happy, and try real hard to bring it back to health.
I'm in no way saying that your heart is not in the right place.
but I am saying that we should not think that it will have any chance at life.
sorry if this offends anyone here.
I'm just saying that we can not save the this chameleon.
just my thoughts,
Harry
#10
Your right, It may die, but it doesn't look like its dieing quite yet. I think it has the potential to be saved and thats why Im looking to get it. The only reason I am even offering a little money is so that they hopefully dont hesitate to hand it over.
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