Ozarking Around with nycRealEstateR

Alex Bresler 2017-05-18

Introduction

This is a quick demonstration of one of my private packages nycRealEstateR. This package provides a wrapper around New York City’s property data ecosystem including, but not limited to, ACRIS, New York City Department of Finance, PLUTO.

This package will not be released for public use but I am open to assisting parties that interest in harnessing the power of the package at my own discretion.

In this tutorial I will analyze the recent activity of a very large construction lender, Arkansas based Bank of the Ozarks to see what they have been up to in 2017.

Background

I was recently exploring new mortgages recordations in Manhattan and I noticed something, that despite a significantly weakening condominium environment Bank of the Ozarks continues to be quite active and even financed a new project for Extell Development earlier in the month.

df_mortgages_recent <-
  nycRealEstateR::get_data_acris_boroughs_docs(
    boroughs = "Manhattan",
    date_start = "2017-05-01",
    date_end = Sys.Date(),
    document_names = c("ASSIGNMENT OF LEASES AND RENTS", "AGREEMENT", "MORTGAGE")
  )

df_mortgages_recent %>%
  filter(dateDocument >= "2017-05-01") %>% 
  select(
    idBlock,
    idBorough,
    idLot,
    dateDocument,
    typeDocument,
    amountDocument,
    dataParties,
    urlACRISDocumentMetadata
  ) %>%
  unnest() %>%
  filter(nameParty %>% str_detect("BANK OF THE OZARKS")) %>%
  select(nameParty,
         typeDocument,
         idBlock,
         urlACRISDocumentMetadata,
         amountDocument) %>%
  distinct() %>% 
  formattable::formattable()

nameParty	typeDocument	idBlock	urlACRISDocumentMetadata	amountDocument
BANK OF THE OZARKS	AGREEMENT	1548	http://a836-acris.nyc.gov/DS/DocumentSearch/DocumentDetail?doc_id=2017051000297011	$85,332,981
BANK OF THE OZARKS	AGREEMENT	1548	http://a836-acris.nyc.gov/DS/DocumentSearch/DocumentDetail?doc_id=2017051000297010	$85,332,981
BANK OF THE OZARKS	MORTGAGE	1548	http://a836-acris.nyc.gov/DS/DocumentSearch/DocumentDetail?doc_id=2017051000297009	$1,106,112

GDELT Sanity Check.

Before delving into the property recordations I wanted to do a quick sanity check and use my gdeltr2 which wraps the incredible GDELT Project to build an interactive trelliscope of extracted mentions of Bank of the Ozarks from all the sources it tracks to see how often the company has been mentioned in recent years.

If you filter on the year and date variables you will see pretty significant mentions for 2017 which is what I expected. Lets move on to the actual activity in New York City.

2017 New York City Activity

Seeing the recent activity got me thinking it would be interesting to explore the company’s activity throughout 2017.

df_ozarks <-
  nycRealEstateR::get_data_acris_parties(search_terms = "BANK OF THE OZARKS",
                                         date_start = "2017-01-01",
                                         date_end = Sys.Date(),
                                         parse_all_documents = FALSE)

df_ozarks %>% 
  count(nameBorough, typeDocument) %>% 
  arrange(desc(n)) %>% 
  formattable::formattable()

nameBorough	typeDocument	n
QUEENS	PARTIAL RELEASE OF MORTGAGE	147
MANHATTAN	UCC3 AMENDMENT	97
MANHATTAN	INITIAL UCC1	27
MANHATTAN	PARTIAL RELEASE OF MORTGAGE	26
MANHATTAN	ASSIGNMENT OF LEASES AND RENTS	24
MANHATTAN	RELEASE	23
MANHATTAN	MORTGAGE	17
QUEENS	RELEASE	14
QUEENS	UCC3 AMENDMENT	13
MANHATTAN	ASSIGNMENT, MORTGAGE	12
QUEENS	ASSIGNMENT, MORTGAGE	11
QUEENS	MORTGAGE	11
MANHATTAN	AGREEMENT	10
MANHATTAN	UCC3 TERMINATION	10
BROOKLYN	ASSIGNMENT, MORTGAGE	9
QUEENS	AGREEMENT	7
BROOKLYN	UCC3 TERMINATION	6
BROOKLYN	PARTIAL RELEASE OF MORTGAGE	3
BROOKLYN	UCC3 AMENDMENT	3
MANHATTAN	SUNDRY MISCELLANEOUS	3
MANHATTAN	UCC3 ASSIGNMENT	3
QUEENS	SUBORDINATION OF MORTGAGE	3
QUEENS	UCC3 TERMINATION	3
BROOKLYN	AGREEMENT	2
MANHATTAN	TERMINATION OF ASSIGN OF L&R	2
QUEENS	ASSIGNMENT OF LEASES AND RENTS	2
QUEENS	INITIAL UCC1	2
QUEENS	SATISFACTION OF MORTGAGE	2
BRONX	AGREEMENT	1
BRONX	ASSIGNMENT, MORTGAGE	1
BROOKLYN	RELEASE	1
BROOKLYN	TERMINATION OF AGREEMENT	1
MANHATTAN	SATISFACTION OF MORTGAGE	1
QUEENS	UCC3 ASSIGNMENT	1

Amount by Document Type and Borough

This will interactively explore the amount recorded by borough and type of document.

library(highcharter)
all <- 
  df_ozarks %>% 
  group_by(nameBorough, typeDocument) %>% 
  summarise(amountTotal = sum(amountDocument, na.rm = T)) %>% 
  ungroup()

categories_grouped <- 
  all %>% 
  group_by(name = nameBorough) %>% 
  do(categories = .$typeDocument) %>% 
  list_parse()

hc <- highchart() %>% 
  hc_xAxis(categories = categories_grouped) %>% 
  hc_add_series(data = all, type = "bar", hcaes(y = amountTotal, color = typeDocument),
                showInLegend = FALSE) %>% 
  hc_add_theme(hc_theme_smpl()) %>% 
  hc_title(text = "Bank of the Ozarks 2017 Recordations") %>% 
  hc_subtitle(text = "Data from ACRIS via nycRealEstateR")

hc

Resolved Property Addresses

Next I am going to use my wrappers for PLUTO and New York City’s Property Tax Database to resolve the ACRIS data with actual addresses and some information about the respective borough, block, lot information.

df_pluto <- nycRealEstateR::import_pluto()
df_lots <- nycRealEstateR::import_tax_lots()

table <- df_ozarks %>% select(idBlock, idLot, idBorough) %>% distinct() %>% 
    inner_join(df_lots %>% select(idBorough, idLot, idBlock, nameAddress, areaBuildingSF, 
        areaLotSF, ratioFAR) %>% bind_rows(df_pluto %>% select(idBorough, idLot, 
        idBlock, nameAddress, areaBuildingSF, areaLotSF, ratioFARMax))) %>% 
    arrange(idBorough, idLot, idBlock)

table

OCR a Random Document

One of the most unique features of nycRealEstateR is its ability to OCR any recorded document on ACRIS. I am going to demonstrate that here by OCR-ing the most recent recorded loan agreement.

ocr_data <- df_ozarks %>% filter(nameBorough == "MANHATTAN") %>% filter(typeDocument == 
    "AGREEMENT") %>% filter(datetimeRecorded == max(datetimeRecorded)) %>% nycRealEstateR::ocr_acris_documents()

OCR Results

Press the first button to the left to unnest the data and explore the text.

listviewer::jsonedit(ocr_data)

All Documents

df_ozarks %>% 
  dplyr::select(-urlACRISDocumentPDF) %>% 
  tidyr::nest(.key = dataTypeDocument, -c(idBorough, nameBorough, typeDocument)) %>%
  arrange(idBorough) %>%
  listviewer::jsonedit()

Next Time

In my next post I will showcase fundManageR to explore what is going on with Bank of Ozarks in the securities market.