THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Cre-X-Mice is a database of Cre transgenic mouse lines and mice genes. Users can search the database by anatomical area, cell type, stage, promoter locus, transgene type, or other properties. Users can also view anatomical areas by stage, or submit transgenic mouse lines.

Graph and download economic data for Delinquency Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets (DRCRELEXFT100S) from Q1 1991 to Q1 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, assets, loans, banks, depository institutions, rate, and USA.

CREB target gene database that uses a multi-layered approach to predict, validate and characterize CREB target genes. For each gene, the database tries to provide the following information: 1. CREB binding sites on the promoters 2. Promoter occupancy by CREB 3. Gene activation by cAMP in tissues CREB seems to occupy a large number of promoters in the genome (up to ~5000 in human), and the profiles for CREB promoter occupancy are very similar in different human tissues. However, only a small proportion of CREB occupied genes are induced by cAMP in any cell type, possibly reflecting the requirement of additional regulatory partners that assist in recruitment of the transcriptional apparatus. To use the database, choose the species, select the table you want to search, leave field (''All'') and type in the gene you want to search. A table listing the search results will be returned, followed by the description of the table. If no search result is returned, try the official gene symbol or gene ID (locuslink number) from NCBI Entrez Gene to search. Sponsors: This work was supported by National Institutes of Health Grants GM RO1-037828 (to M.M.) and DK068655 (to R.A.Y.).

Cloud-only regimes (and 3 sub-regimes of CR15) originally derived from Terra and Aqua observations in 50S-50N, and corresponding "regime numbers on map" files assigned to 60S-60N domain are provided (see Jin et al. 2021, doi: 10.1175/JAMC-D-20-0253.1). The zip file also includes sample python codes to calculate regime mean SWCRE/LWCRE for the study of observational CRE feedback (Jin et al. 2023, J. of Climate, submitted).

Graph and download economic data for Real Estate Loans: Commercial Real Estate Loans, All Commercial Banks (CREACBM027NBOG) from Jun 2004 to May 2025 about real estate, commercial, loans, banks, depository institutions, and USA.

Abstract

The 2019 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2019 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). Local planners, policy makers, public health officials, and community stakeholders can use the estimates as one tool to help assess the potential resiliency of communities and plan mitigation and recovery strategies. The CRE uses small area modeling techniques. These techniques are flexible and can easily be modified for a broad range of uses (hurricanes, tornadoes, floods, economic recovery etc.).

The table is consisted of geographic infomation including state county and tract, and variables indcating risk factors. The CRE groups the population estimates into three categories: zero risk factors, one-two risk factors, and three plus risk factors. The data file includes the population estimate, estimate margin of error, rate, and rate margin of error for each of the three categories.

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q3 2024 about real estate, commercial, rate, and USA.

The Property Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making in the real estate sector. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility; the increasing need for sophisticated analytics to optimize investment strategies amongst both SMEs and large enterprises; and the proliferation of readily available data sources enriching the insights generated by these platforms. Technological advancements, such as AI and machine learning integration, further enhance the market's capabilities, enabling predictive analytics and improved risk assessment. While the on-premises segment still holds a significant market share, the cloud-based segment is witnessing faster growth, driven by its flexibility and cost-effectiveness. Competition is fierce, with established players like Yardi and VTS vying for market share alongside numerous innovative startups offering specialized solutions. Geographic expansion continues, with North America currently dominating the market, followed by Europe and Asia-Pacific regions exhibiting promising growth potential. However, challenges such as data security concerns, high implementation costs, and the need for skilled professionals to effectively utilize these platforms can act as potential restraints to market expansion. Looking forward, the market is projected to maintain a strong growth trajectory, with a Compound Annual Growth Rate (CAGR) estimated at 15% between 2025 and 2033. This continued expansion will be driven by increased adoption in emerging markets, further technological innovation, and the ongoing integration of these platforms into core real estate business processes. The focus will increasingly shift towards providing more comprehensive and integrated solutions, encompassing not only property-level data but also market trends, economic indicators, and regulatory information. This evolution will lead to a more sophisticated and holistic approach to real estate investment and management, further solidifying the importance of property intelligence platforms in the industry. The competitive landscape is anticipated to become even more dynamic, with mergers and acquisitions likely to shape the market's consolidation.

Database of microarray analysis of twelve major classes of fluorescent labeled neurons within the adult mouse forebrain that provide the first comprehensive view of gene expression differences. The publicly available datasets demonstrate a profound molecular heterogeneity among neuronal subtypes, represented disproportionately by gene paralogs, and begin to reveal the genetic programs underlying the fundamental divisions between neuronal classes including that between glutamatergic and GABAergic neurons. Five of the 12 populations were chosen from cingulate cortex and included several subtypes of GABAergic interneurons and pyramidal neurons. The remaining seven were derived from the somatosensory cortex, hippocampus, amygdala and thalamus. Using these expression profiles, they were able to construct a taxonomic tree that reflected the expected major relationships between these populations, such as the distinction between cortical interneurons and projection neurons. The taxonomic tree indicated highly heterogeneous gene expression even within a single region. This dataset should be useful for the classification of unknown neuronal subtypes, the investigation of specifically expressed genes and the genetic manipulation of specific neuronal circuit elements. Datasets: * Full: Here you can query gene expression results for the neuronal populations * Strain: Here you can query the same expression results accessed under the full checkbox, with one additional population (CT6-CG2) included as a control for the effects of mouse strain. This population is identical to CT6-CG (YFPH) except the neurons were derived from wild-type mice of three distinct strains: G42, G30, and GIN. * Arlotta: Here you can query the same expression results accessed under the full checkbox, with nine additional populations from the dataset of Arlotta et al., 2005. These populations were purified by FACS after retrograde labeling with fluorescent microspheres. Populations are designated by the prefix ACS for corticospinal neurons, ACC for corticocallosal neurons and ACT for corticotectal neurons, followed by the suffix E18 for gestational age 18 embryos, or P3, P6 and P14 for postnatal day 3, 6 and 14 pups. For each successful gene query the following information is returned: # Signal level line plot: Signal level is plotted on Y-axis (log base 2) for each sample. Samples include the thirty six representing the twelve populations profiled in Sugino et al. In addition, six samples from homogenized (=dissociated and but not sorted) cortex are included representing two different strains: G42-HO is homogenate from strain G42, GIN-HO is homogenate from stain GIN. # Signal level raster plots: Signal level is represented by color (dark red is low, bright red is high) for all samples. Color scale is set to match minimum (dark red) and maximum (bright yellow) signal levels within the displayed set of probe sets. # Scaled signal level raster plots: Same as 2) except color scale is adjusted separately for each gene according to its maximum and minimum signal level. # Table: Basic information about the returned probe sets: * Affymetrix affyid of probe set * NCBI gene symbol, NCBI gene name * NCBI geneID * P-value score from ANOVA for each gene is also given if available (_anv column). P-value represents the probability that there is no difference in the expression across cell types.

The Commercial Real Estate Technology (CRE Tech) market is experiencing robust growth, driven by increasing adoption of digital solutions to streamline operations, enhance efficiency, and improve decision-making across the commercial real estate sector. The market, estimated at $25 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by factors such as the rising demand for property management software, the growing need for data-driven insights, and the increasing penetration of PropTech solutions across various CRE segments, including office, retail, industrial, and multifamily. Key trends include the integration of Artificial Intelligence (AI) and Machine Learning (ML) for property valuation, predictive analytics for risk management, and the rise of smart building technologies enhancing energy efficiency and tenant experience. The increasing adoption of cloud-based solutions and the growing emphasis on data security are also shaping the market landscape. However, challenges remain. High initial investment costs for implementing new technologies, integration complexities with existing systems, and the need for skilled professionals to manage and utilize these advanced tools can hinder wider adoption. Furthermore, concerns surrounding data privacy and security continue to present a significant hurdle for market expansion. Despite these restraints, the long-term outlook for the CRE Tech market remains positive, driven by continuous innovation, increasing investor interest, and the inherent need for greater efficiency and transparency within the commercial real estate sector. The consolidation of smaller players through mergers and acquisitions is also expected to increase market concentration and accelerate growth in the coming years. A projected Compound Annual Growth Rate (CAGR) of 15% suggests a market size exceeding $70 Billion by 2033. This growth is anticipated across diverse geographical regions, with North America and Europe maintaining significant market share due to early adoption and established PropTech ecosystems.

The commercial real estate (CRE) compliance market is expected to grow significantly in the coming years, driven by increasing regulatory requirements and the need for greater transparency in the industry. The market is expected to reach a value of USD XXX million by 2033, with a CAGR of XX% over the forecast period. The growth of the market is also being fueled by the increasing adoption of cloud-based solutions and the growing use of data analytics to improve compliance processes. Key trends in the CRE compliance market include the increasing use of technology to automate compliance processes, the growing demand for compliance training and support services, and the increasing focus on environmental, social, and governance (ESG) compliance. The market is also being driven by the increasing number of mergers and acquisitions in the CRE industry, which is creating a need for greater compliance oversight. The major players in the CRE compliance market include MRI Software, Yardi Systems, RealPage, AppFolio, Buildium, Entrata, InstaLend, CREXi, Reonomy, and VTS. The market is expected to be highly competitive in the coming years, with these players vying for market share through innovation and expansion.

The Community Resilience Estimates (CRE) program provides an easily understood metric for how socially vulnerable every neighborhood in the United States is to the impacts of disasters.This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census, CRE, and ACS when using this data.Overview:Community resilience is the capacity of individuals and households within a community to prepare, absorb, respond, and recover from a disaster. Local planners, policy makers, public health officials, emergency managers, and community stakeholders need a variety of estimates to help assess the potential resiliency and vulnerabilities of communities and their constituent populations to help prepare and plan mitigation, recovery, and response strategies. Community Resilience Estimates (CRE) focuses on developing a tool to identify socio-economic vulnerabilities within populations. The 2022 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2022 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). The CRE uses small area modeling techniques that can be used for a broad range of disaster related events (hurricanes, tornadoes, floods, economic shocks, etc.) to identify population concentrations likely to be relatively more impacted by and have greater difficulties overcoming disasters. The end result is a data product which measures vulnerability more accurately and timely. Data:The ACS is a nationally representative survey with data on the characteristics of the U.S. population. The sample is selected from all counties and county-equivalents and has a sample size of about 3.5 million housing units each year. It is the premier source for timely and detailed population and housing information about our nation and its communities. We also use auxiliary data from the PEP, the Census Bureau’s program that produces and publishes estimates of the population living at a given time within a geographic entity in the U.S. and Puerto Rico. We use population data from the PEP by age group, race and ethnicity, and sex. Since the PEP does not go down to the census tract level, the CRE uses the Public Law 94-171 summary files (PL94) and Demographic Housing Characteristics File (DHC) tables from the 2020 Decennial Census to help produce the population base estimates. Once the weighted estimates are tabulated, small area modeling techniques are used to create the estimates for the CRE. Components of Social Vulnerability (SV): Resilience to a disaster is partly determined by the components of social vulnerability exhibited within a community’s population. To measure these components and construct the community resilience estimates, we designed population estimates based on individual- and household-level components of social vulnerability. These components are binary indicators or variables that add up to a maximum of 10 possible components using data from the ACS. The specific ACS-defined measures we use are as follows: Components of Social Vulnerability (SV) for Households (HH) and Individuals (I):SV 1: Income-to-Poverty Ratio (IPR) < 130 percent (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH). SV 3: Unit-level crowding with >= 0.75 persons per room (HH). SV 4: Communication barrier defined as either: Limited English-speaking households1 (HH) orNo one in the household over the age of 16 with a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH). SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: No vehicle access (HH). SV 10: Households without broadband internet access (HH). Each individual is assigned a 0 or 1 for each of the components based upon their individual or household attributes listed above. It is important to note that SV 4 is not double flagged. An individual will be assigned a 1, if either of the characteristics is true for their household. For example, if a household is linguistically isolated and no one over the age of 16 has attained a high school diploma or more education, the household members are only flagged once. The result is an index that produces aggregate-level (tract, county, and state) small area estimates: the CRE. The CRE provide an estimate for the number of people with a specific number of social vulnerabilities. In its current data file layout form, the estimates are categorized into three groups: zero , one-two, or three plus social vulnerability components. Differences with CRE 2021:The number of census tracts have increased from 84,414 in CRE 2021 to 84,415 in CRE 2022. This is due to the boundary changes in Connecticut implemented in 2022 census data products. To accommodate the boundary change, Connecticut also now has nine planning regions instead of eight counties in CRE 2022.To avoid confusion, the modeled rates are now set to equal zero in CRE 2022 for geographic areas with zero population in universe. To improve the population base estimates, CRE 2022 uses more detailed decennial estimates from the 2020 DHC in addition to PL94, whereas CRE 2021 just used PL94 due to availability at the time. See “2022 Community Resilience Estimates: Detailed Technical Documentation” for more information. Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). This dataset does not contain values for Puerto Rico or Island Areas at any level of geography.Further Information:Community Resilience Estimates Program Website https://www.census.gov/programs-surveys/community-resilience-estimates.htmlCommunity Resilience Estimates Technical Documentation https://census.gov/programs-surveys/community-resilience-estimates/technical-documentation.htmlFor Data Questionssehsd.cre@census.gov

In Bacillus subtilis and its relatives carbon catabolite control, a mechanism enabling to reach maximal efficiency of carbon and energy sources metabolism, is achieved by the global regulator CcpA (carbon catabolite protein A). CcpA in a complex with HPr-Ser-P (seryl-phosphorylated form of histidine-containing protein, HPr) binds to operator sites called catabolite responsive elements, cre. Depending on the cre box position relative to the promoter, the CcpA/HPr-Ser-P complex can either act as a positive or a negative regulator. The cre boxes are highly degenerate semi-palindromes with a lowly conserved consensus sequence. So far, studies aimed at revealing how CcpA can bind such diverse sites were focused on the analysis of single cre boxes. In this study, a genome-wide analysis of cre sites was performed in order to identify differences in cre sequence and position, which determine their binding affinity. The transcriptomes of B. subtilis cultures with three different CcpA expression levels were compared. The higher the amount of CcpA in the cells, the more operons possessing cre sites were differentially regulated. The cre boxes that mediated regulation at low CcpA levels were designated as strong (high affinity) and those which responded only to high amounts of CcpA, as weak (low affinity). Differences in the sequence and position in relation to the transcription start site between strong and weak cre boxes were revealed. Certain residues at specific positions in the cre box as well as, to a certain extent, a more palindromic nature of cre sequences and the location of cre in close vicinity to the transcription start site contribute to the strength of CcpA-dependent regulation. The main factors contributing to cre regulatory efficiencies, enabling subtle differential control of various subregulons of the CcpA regulon, are identified.

The Commercial Real Estate (CRE) Compliance Tool market is experiencing robust growth, driven by increasing regulatory complexity, heightened investor scrutiny, and the need for streamlined compliance processes. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $6.5 billion by 2033. Key drivers include the growing adoption of cloud-based solutions, the rising demand for automated compliance workflows, and a focus on reducing compliance-related risks and penalties. Emerging trends, such as AI-powered compliance monitoring and integration with other CRE software platforms, are further accelerating market expansion. While data security concerns and the initial investment costs associated with implementing these tools represent potential restraints, the overall market outlook remains positive, fueled by the long-term benefits of improved efficiency and reduced liability. The market is segmented by deployment type (cloud-based, on-premise), functionality (lease management, environmental compliance, tenant screening), and company size (small, medium, large). Major players such as MRI Software, Yardi Systems, RealPage, AppFolio, Buildium, Entrata, InstaLend, CREXi, Reonomy, and VTS are shaping the competitive landscape through continuous innovation and strategic partnerships. The competitive landscape is marked by both established players and emerging startups. Established players leverage their extensive client bases and integrated platforms to maintain a strong market position. Startups, however, are introducing innovative solutions and focusing on niche market segments. The North American market currently dominates global share, benefiting from a mature CRE sector and stringent regulatory frameworks. However, Europe and Asia-Pacific are experiencing significant growth, driven by increasing regulatory pressure and technological advancements. The forecast period (2025-2033) anticipates continued market expansion, with a potential shift towards more specialized and AI-driven solutions as the industry seeks to optimize efficiency and mitigate compliance risks. Strategic acquisitions and partnerships will likely play a key role in shaping the market's future.

European Union EU: NPL Ratio: Loans & Advances at Amortised Cost: ow Commercial Real Estate (CRE) data was reported at 4.300 % in Dec 2024. This stayed constant from the previous number of 4.300 % for Sep 2024. European Union EU: NPL Ratio: Loans & Advances at Amortised Cost: ow Commercial Real Estate (CRE) data is updated quarterly, averaging 4.550 % from Sep 2019 (Median) to Dec 2024, with 22 observations. The data reached an all-time high of 7.600 % in Mar 2020 and a record low of 3.800 % in Mar 2023. European Union EU: NPL Ratio: Loans & Advances at Amortised Cost: ow Commercial Real Estate (CRE) data remains active status in CEIC and is reported by European Banking Authority. The data is categorized under Global Database’s European Union – Table EU.KB023: European Banking Authority: Non-Performing Loans Ratio: Loans and Advances.

The Commercial Real Estate Analysis Software market is experiencing robust growth, driven by increasing demand for efficient property management and investment analysis tools. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility, the growing need for data-driven decision-making in real estate investments, and the increasing complexity of property valuations and market analysis. Technological advancements, such as AI-powered analytics and predictive modeling, are further enhancing the capabilities of these software solutions, leading to improved accuracy and efficiency in property assessments and portfolio management. The market is segmented by application (office spaces, retail spaces, hospitality, healthcare, and others) and deployment type (on-premises and cloud-based). Cloud-based solutions are gaining significant traction due to their cost-effectiveness and flexibility. Key players in the market are continuously innovating to offer advanced features, integrations, and data visualization tools to cater to the evolving needs of real estate professionals. The market is geographically diverse, with North America and Europe currently holding significant market shares, but regions like Asia-Pacific are expected to witness substantial growth in the coming years due to increasing urbanization and infrastructure development. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and improvements in the technology and features available. The projected CAGR, while not explicitly stated, can be reasonably estimated based on market trends within the real estate technology sector. Considering the technological advancements and increasing demand, a conservative CAGR of 8-10% for the forecast period (2025-2033) seems plausible. This assumes a moderate pace of adoption across various segments and regions. Challenges such as high initial investment costs for some software solutions and the need for skilled professionals to effectively utilize these tools could potentially act as restraints on market growth. However, the ongoing technological advancements and the increasing need for efficient property management are likely to overcome these obstacles, leading to sustained growth in the market. Further segmentation of the market based on property type (residential, commercial, industrial) and user type (brokers, investors, property managers) could provide a more granular understanding of market dynamics.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 19, 2012. Due to budgetary constraints, the National Center for Biotechnology Information (NCBI) has discontinued support for the NCBI GENSAT database, and it has been removed from the Entrez System. The Gene Expression Nervous System Atlas (GENSAT) project involves the large-scale creation of transgenic mouse lines expressing green fluorescent protein (GFP) reporter or Cre recombinase under control of the BAC promoter in specific neural and glial cell populations. BAC expression data for all the lines generated (over 1300 lines) are available in online, searchable databases (www.gensat.org and the Database of GENSAT BAC-Cre driver lines). If you have any specific questions, please feel free to contact us at info_at_ncbi.nlm.nih.gov The GENSAT project aims to map the expression of genes in the central nervous system of the mouse, using both in situ hybridization and transgenic mouse techniques. Search criteria include gene names, gene symbols, gene aliases and synonyms, mouse ages, and imaging protocols. Mouse ages are restricted to E10.5 (embryonic day 10.5), E15.5 (embryonic day 15.5), P7 (postnatal day 7), and Adult (adult). The project focuses on two techniques * Evaluation of unmodified mice lines for expression of a given gene using radiolabelled riboprobes and in-situ hybridization. * Creation of transgenic mice lines containing a BAC construct that expresses a marker gene in the same environment as the native gene

US And Japan IoT SaaS Market For CRE size was valued at USD 1,421.14 Million in 2023 and is projected to reach USD 3,027.96 Million by 2031, growing at a CAGR of 10.20% from 2024 to 2031.

US And Japan IoT SaaS Market For CRE Overview

The proliferation of IoT devices and sensors in CRE has ushered in an era of unprecedented data collection and connectivity. While this abundance of data presents numerous opportunities for optimizing building operations and enhancing tenant experiences, it also raises significant concerns about privacy, cybersecurity, and regulatory compliance. One of the primary concerns associated with IoT deployments in CRE is the potential for unauthorized access and data breaches. IoT devices are often connected to the internet and transmit data wirelessly, making them vulnerable to cyberattacks and hacking attempts. Malicious actors may exploit security vulnerabilities in IoT devices to gain unauthorized access to sensitive data or disrupt building operations. For example, hackers could infiltrate HVAC systems to manipulate temperature settings or gain access to security cameras to monitor building occupants without consent.

IoT SaaS implementations must adhere to various regulatory mandates concerning data privacy and security. Numerous jurisdictions impose strict regulations governing the collection, retention, and processing of personal data, such as the California Consumer Privacy Act (CCPA) in the United States. Failure to comply with these statutes can lead to significant penalties, fines, and reputational harm for entities. Similarly, adherence to regulations like the Energy Policy Act (EP Act) and local building codes necessitates meticulous planning and adherence to established standards and best practices. Violations of such regulations can incur severe penalties, fines, and legal liabilities for property owners and managers. Additionally, Japan maintains rigorous data protection laws, such as the Act on the Protection of Personal Information (APPI), which govern the handling of personal data and require organizations to obtain consent from individuals for data processing.

Decoding the functional connectivity of the nervous system is facilitated by transgenic methods that express a genetically encoded reporter or effector in specific neurons; however, most transgenic lines show broad spatiotemporal and cell-type expression. Increased specificity can be achieved using intersectional genetic methods which restrict reporter expression to cells that co-express multiple drivers, such as Gal4 and Cre. To facilitate intersectional targeting in zebrafish, we have generated more than 50 new Cre lines, and co-registered brain expression images with the Zebrafish Brain Browser, a cellular resolution atlas of 264 transgenic lines. Lines labeling neurons of interest can be identified using a web-browser to perform a 3D spatial search (zbbrowser.com). This resource facilitates the design of intersectional genetic experiments and will advance a wide range of precision circuit-mapping studies.

The Lease Abstraction Service market is experiencing robust growth, driven by the increasing complexity of commercial real estate portfolios and a rising need for efficient data management. The market's expansion is fueled by several key factors. Firstly, the surge in digital transformation within the real estate sector is pushing companies to adopt automated solutions for lease abstraction, eliminating manual processes and reducing human error. Secondly, the growing demand for data-driven decision-making among real estate investors and portfolio managers is creating a high demand for accurate and readily available lease data. Finally, the increasing regulatory scrutiny and compliance requirements across the globe necessitate meticulous lease record keeping, further boosting the adoption of these services. We estimate the market size in 2025 to be around $500 million, based on typical market growth rates for similar technology-driven services in the real estate sector. Considering a plausible CAGR of 15% (a conservative estimate considering the technological advancements and market demand), this would project significant expansion over the forecast period of 2025-2033. However, despite the positive outlook, several challenges restrain market growth. The initial investment in software and technology can be significant, potentially deterring smaller firms. Furthermore, data security and privacy concerns surrounding sensitive lease information need to be addressed effectively to ensure widespread adoption. The market is also subject to competitive pressures, with several established players and new entrants vying for market share. To overcome these challenges, companies are focusing on improving data security, offering flexible pricing models, and developing user-friendly solutions to broaden their appeal. The segmentation of the market reflects this dynamism, with specialized services catering to specific client needs and different technology solutions available. The key players mentioned – including Altus Group, Accruent, and others – are actively investing in research and development to improve their offerings and solidify their market positions.